SwePub - sökning: WFRF:(Lindsten Fredrik)

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1.	Gunnarsson, Fredrik, et al. (författare) Particle filtering for network-based positioning terrestrial radio networks 2014 Ingår i: Data Fusion & Target Tracking 2014: Algorithms and Applications (DF&TT 2014), IET Conference on. - : Institution of Engineering and Technology. - 9781849198639 Konferensbidrag (refereegranskat)abstract There is strong interest in positioing in wireless networks, partly to support end user service needs, but also to support network management with network-based network information. The focus in this paper is on the latter, while using measurements that are readily available in wireless networks. We show how thesignal direction of departure and inter-distance between the base station and the mobile terminal can be estimated, and how particle filters and smoothers can be used to post-process the measurements. The methods are evaluated in a live 3GPP LTE network with promising results inlcuding position error medians of less than 100 m.
2.	Lindsten, Fredrik, et al. (författare) Conflict Detection Metrics for Aircraft Sense and Avoid Systems 2009 Ingår i: Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. - Linköping : Linköping University Electronic Press. - 9783902661463 ; , s. 65-70 Konferensbidrag (refereegranskat)abstract The task of an airborne collision avoidance system is to continuously evaluate the risk of collision and in the case of too high risk initiate an evasive action. The traditional way to assess risk is to focus on a critical point of time. A recently proposed alternative is to evaluate the cumulated risk over time. It is the purpose of this contribution to evaluate the difference between these two concepts and also to validate an approximate method for computing the cumulated risk, suitable for real-time implementations. For this purpose, random scenarios are generated from stochastic models created from observed conflicts. A realistic tracking filter, based on angle-only measurements, is used to produce uncertain state estimates which are used for risk assessment. It is shown that the cumulated risk is much more robust to estimation accuracy than the maximum of the instantaneous risk. The intended application is for unmanned aerial vehicles to be used in civilian airspace, but a real mid-air collision scenario between two traffic aircraft is studied as well.
3.	Lindsten, Fredrik, et al. (författare) Geo-Referencing for UAV Navigation using Environmental Classification 2010 Ingår i: Proceedings of the 2010 IEEE International Conference on Robotics and Automation. - Linköping : Linköping University Electronic Press. - 9781424450404 - 9781424450381 ; , s. 1420-1425 Konferensbidrag (refereegranskat)abstract A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.
4.	Lindsten, Fredrik, 1984- (författare) Particle filters and Markov chains for learning of dynamical systems 2013 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies.
5.	Lindsten, Fredrik (författare) Rao-Blackwellised particle methods for inference and identification 2011 Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract We consider the two related problems of state inference in nonlinear dynamical systems and nonlinear system identification. More precisely, based on noisy observations from some (in general) nonlinear and/or non-Gaussian dynamical system, we seek to estimate the system state as well as possible unknown static parameters of the system. We consider two different aspects of the state inference problem, filtering and smoothing, with the emphasis on the latter. To address the filtering and smoothing problems, we employ sequential Monte Carlo (SMC) methods, commonly referred to as particle filters (PF) and particle smoothers (PS).Many nonlinear models encountered in practice contain some tractable substructure. If this is the case, a natural idea is to try to exploit this substructure to obtain more accurate estimates than what is provided by a standard particle method. For the filtering problem, this can be done by using the well-known Rao-Blackwellised particle filter (RBPF). In this thesis, we analyse the RBPF and provide explicit expressions for the variance reduction that is obtained from Rao-Blackwellisation. Furthermore, we address the smoothing problem and develop a novel Rao-Blackwellised particle smoother (RBPS), designed to exploit a certain tractable substructure in the model.Based on the RBPF and the RBPS we propose two different methods for nonlinear system identification. The first is a recursive method referred to as the Rao-Blackwellised marginal particle filter (RBMPF). By augmenting the state variable with the unknown parameters, a nonlinear filter can be applied to address the parameter estimation problem. However, if the model under study has poor mixing properties, which is the case if the state variable contains some static parameter, SMC filters such as the PF and the RBPF are known to degenerate. To circumvent this we introduce a so called “mixing” stage in the RBMPF, which makes it more suitable for models with poor mixing properties.The second identification method is referred to as RBPS-EM and is designed for maximum likelihood parameter estimation in a type of mixed linear/nonlinear Gaussian statespace models. The method combines the expectation maximisation (EM) algorithm with the RBPS mentioned above, resulting in an identification method designed to exploit the tractable substructure present in the model.
6.	Özkan, Emre, et al. (författare) Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems 2015 Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 63:3, s. 754-765 Tidskriftsartikel (refereegranskat)abstract We present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of particle filters which makes the method applicable to a wide range of non-linear models. To exploit the inherent structure of JMNLS, we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is marginalized out analytically. This results in an efficient implementation of the algorithm and reduces the estimation error variance. The proposed RBPF is then used to compute, recursively in time, smoothed estimates of complete data sufficient statistics. Together with the online expectation maximization algorithm, this enables recursive identification of unknown model parameters including the transition probability matrix. The method is also applicable to online identification of jump Markov linear systems(JMLS). The performance of the method is illustrated in simulations and on a localization problem in wireless networks using real data.
7.	Ahmadian, Amirhossein, 1992-, et al. (författare) Enhancing Representation Learning with Deep Classifiers in Presence of Shortcut 2023 Ingår i: Proceedings of IEEE ICASSP 2023. Konferensbidrag (refereegranskat)abstract A deep neural classifier trained on an upstream task can be leveraged to boost the performance of another classifier in a related downstream task through the representations learned in hidden layers. However, presence of shortcuts (easy-to-learn features) in the upstream task can considerably impair the versatility of intermediate representations and, in turn, the downstream performance. In this paper, we propose a method to improve the representations learned by deep neural image classifiers in spite of a shortcut in upstream data. In our method, the upstream classification objective is augmented with a type of adversarial training where an auxiliary network, so called lens, fools the classifier by exploiting the shortcut in reconstructing images. Empirical comparisons in self-supervised and transfer learning problems with three shortcut-biased datasets suggest the advantages of our method in terms of downstream performance and/or training time.
8.	Ahmadian, Amirhossein, 1992-, et al. (författare) Likelihood-free Out-of-Distribution Detection with Invertible Generative Models 2021 Ingår i: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021). - California : International Joint Conferences on Artificial Intelligence Organization. Konferensbidrag (refereegranskat)abstract Likelihood of generative models has been used traditionally as a score to detect atypical (Out-of-Distribution, OOD) inputs. However, several recent studies have found this approach to be highly unreliable, even with invertible generative models, where computing the likelihood is feasible. In this paper, we present a different framework for generative model--based OOD detection that employs the model in constructing a new representation space, instead of using it directly in computing typicality scores, where it is emphasized that the score function should be interpretable as the similarity between the input and training data in the new space. In practice, with a focus on invertible models, we propose to extract low-dimensional features (statistics) based on the model encoder and complexity of input images, and then use a One-Class SVM to score the data. Contrary to recently proposed OOD detection methods for generative models, our method does not require computing likelihood values. Consequently, it is much faster when using invertible models with iteratively approximated likelihood (e.g. iResNet), while it still has a performance competitive with other related methods
9.	Ahmadian, Amirhossein, 1992-, et al. (författare) Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio 2024 Ingår i: International Conference on Artificial Intelligence and Statistics 2024, Proceedings of Machine Learning Research. Konferensbidrag (refereegranskat)
10.	Alenlöv, Johan, et al. (författare) Pseudo-Marginal Hamiltonian Monte Carlo 2021 Ingår i: Journal of machine learning research. - : MICROTOME PUBL. - 1532-4435 .- 1533-7928. ; 22 Tidskriftsartikel (refereegranskat)abstract Bayesian inference in the presence of an intractable likelihood function is computationally challenging. When following a Markov chain Monte Carlo (MCMC) approach to approximate the posterior distribution in this context, one typically either uses MCMC schemes which target the joint posterior of the parameters and some auxiliary latent variables, or pseudo-marginal Metropolis-Hastings (MH) schemes. The latter mimic a MH algorithm targeting the marginal posterior of the parameters by approximating unbiasedly the intractable likelihood. However, in scenarios where the parameters and auxiliary variables are strongly correlated under the posterior and/or this posterior is multimodal, Gibbs sampling or Hamiltonian Monte Carlo (HMC) will perform poorly and the pseudo-marginal MH algorithm, as any other MH scheme, will be inefficient for high-dimensional parameters. We propose here an original MCMC algorithm, termed pseudo-marginal HMC, which combines the advantages of both HMC and pseudo-marginal schemes. Specifically, the PM-HMC method is controlled by a precision parameter N, controlling the approximation of the likelihood and, for any N, it samples the marginal posterior of the parameters. Additionally, as N tends to infinity, its sample trajectories and acceptance probability converge to those of an ideal, but intractable, HMC algorithm which would have access to the intractable likelihood and its gradient. We demonstrate through experiments that PM-HMC can outperform significantly both standard HMC and pseudo-marginal MH schemes.
11.	Andersson Naesseth, Christian, et al. (författare) Capacity estimation of two-dimensional channels using Sequential Monte Carlo 2014 Ingår i: 2014 IEEE Information Theory Workshop. ; , s. 431-435 Konferensbidrag (refereegranskat)abstract We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D (1, ∞) run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time.
12.	Andersson Naesseth, Christian, et al. (författare) High-Dimensional Filtering Using Nested Sequential Monte Carlo 2019 Ingår i: IEEE Transactions on Signal Processing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1053-587X .- 1941-0476. ; 67:16, s. 4177-4188 Tidskriftsartikel (refereegranskat)abstract Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions. We propose nested sequential Monte Carlo, a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correctSMCalgorithm. This way, we can compute an "exact approximation" of, e. g., the locally optimal proposal, and extend the class of models forwhichwe can perform efficient inference using SMC. We showimproved accuracy over other state-of-the-art methods on several spatio-temporal state-space models.
13.	Andersson Naesseth, Christian, 1986- (författare) Machine learning using approximate inference : Variational and sequential Monte Carlo methods 2018 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models.There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them.First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.
14.	Andersson Naesseth, Christian, et al. (författare) Nested Sequential Monte Carlo Methods 2015 Ingår i: Proceedings of The 32nd International Conference on Machine Learning. - : Journal of Machine Learning Research (Online). - 9781510810587 ; , s. 1292-1301 Konferensbidrag (refereegranskat)abstract We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.
15.	Andersson Naesseth, Christian, et al. (författare) Sequential Monte Carlo for Graphical Models 2014 Ingår i: Advances in Neural Information Processing Systems. ; , s. 1862-1870 Konferensbidrag (refereegranskat)abstract We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.
16.	Bunch, Pete, et al. (författare) Particle Gibbs with refreshed backward simulation 2015 Ingår i: Proceedings of the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467369978 Konferensbidrag (refereegranskat)abstract The particle Gibbs algorithm can be used for Bayesian parameter estimation in Markovian state space models. Sometimes the resulting Markov chains mix slowly when the component particle filter suffers from degeneracy. This effect can be somewhat alleviated using backward simulation. In this paper we show how a simple modification to this scheme, which we refer to as refreshed backward simulation, can further improve the mixing. This works by sampling new state values simultaneously with the corresponding ancestor indexes. Although the necessary conditional distributions cannot be sampled directly, we provide suitable Markov kernels which target them. The efficacy of this new scheme is demonstrated with a simulation example.
17.	Calafat, Francisco M., et al. (författare) Coherent modulation of the sea-level annual cycle in the United States by Atlantic Rossby waves 2018 Ingår i: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 9 Tidskriftsartikel (refereegranskat)abstract Changes in the sea-level annual cycle (SLAC) can have profound impacts on coastal areas, including increased flooding risk and ecosystem alteration, yet little is known about the magnitude and drivers of such changes. Here we show, using novel Bayesian methods, that there are significant decadal fluctuations in the amplitude of the SLAC along the United States Gulf and Southeast coasts, including an extreme event in 2008-2009 that is likely (probability = 68%) unprecedented in the tide-gauge record. Such fluctuations are coherent along the coast but decoupled from deep-ocean changes. Through the use of numerical and analytical ocean models, we show that the primary driver of these fluctuations involves incident Rossby waves that generate fast western-boundary waves. These Rossby waves project onto the basin-wide upper mid-ocean transport (top 1000 m) leading to a link with the SLAC, wherein larger SLAC amplitudes coincide with enhanced transport variability.
18.	Dahlin, Johan, 1986- (författare) Accelerating Monte Carlo methods for Bayesian inference in dynamical models 2016 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ.The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). That is, strategies for reducing the computational effort while keeping or improving the accuracy. A major part of the thesis is devoted to proposing such strategies for the MCMC method known as the particle Metropolis-Hastings (PMH) algorithm. We investigate two strategies: (i) introducing estimates of the gradient and Hessian of the target to better tailor the algorithm to the problem and (ii) introducing a positive correlation between the point-wise estimates of the target.Furthermore, we propose an algorithm based on the combination of SMC and Gaussian process optimisation, which can provide reasonable estimates of the posterior but with a significant decrease in computational effort compared with PMH. Moreover, we explore the use of sparseness priors for approximate inference in over-parametrised mixed effects models and autoregressive processes. This can potentially be a practical strategy for inference in the big data era. Finally, we propose a general method for increasing the accuracy of the parameter estimates in non-linear state space models by applying a designed input signal.
19.	Dahlin, Johan, 1986-, et al. (författare) Hierarchical Bayesian approaches for robust inference in ARX models 2012 Ingår i: Proceedings from the 16th IFAC Symposium on System Identification, 2012. - : International Federation of Automatic Control. - 9783902823069 ; , s. 131-136 Konferensbidrag (refereegranskat)abstract Gaussian innovations are the typical choice in most ARX models but using other distributions such as the Student's t could be useful. We demonstrate that this choice of distribution for the innovations provides an increased robustness to data anomalies, such as outliers and missing observations. We consider these models in a Bayesian setting and perform inference using numerical procedures based on Markov Chain Monte Carlo methods. These models include automatic order determination by two alternative methods, based on a parametric model order and a sparseness prior, respectively. The methods and the advantage of our choice of innovations are illustrated in three numerical studies using both simulated data and real EEG data.
20.	Dahlin, Johan, 1986-, et al. (författare) Particle filter-based Gaussian process optimisation for parameter inference 2014 Ingår i: Proceedings of the 19th IFAC World Congress, 2014. - 9783902823625 ; , s. 8675-8680 Konferensbidrag (refereegranskat)abstract We propose a novel method for maximum-likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using these log-likelihood estimates, a surrogate objective function is created by utilizing a Gaussian process model. Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model. The method is profiled on two state space models with good performance both considering accuracy and computational cost.
21.	Dahlin, Johan, 1986-, et al. (författare) Particle Metropolis-Hastings using gradient and Hessian information 2015 Ingår i: Statistics and computing. - : Springer. - 0960-3174 .- 1573-1375. ; 25:1, s. 81-92 Tidskriftsartikel (refereegranskat)abstract Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining MCMC and particle filtering. The latter is used to estimate the intractable likelihood. In its original formulation, PMH makes use of a marginal MCMC proposal for the parameters, typically a Gaussian random walk. However, this can lead to a poor exploration of the parameter space and an inefficient use of the generated particles.We propose two alternative versions of PMH that incorporate gradient and Hessian information about the posterior into the proposal. This information is more or less obtained as a byproduct of the likelihood estimation. Indeed, we show how to estimate the required information using a fixed-lag particle smoother, with a computational cost growing linearly in the number of particles. We conclude that the proposed methods can: (i) decrease the length of the burn-in phase, (ii) increase the mixing of the Markov chain at the stationary phase, and (iii) make the proposal distribution scale invariant which simplifies tuning.
22.	Dahlin, Johan, 1986-, et al. (författare) Particle Metropolis Hastings using Langevin Dynamics 2013 Ingår i: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing. - : IEEE conference proceedings. ; , s. 6308-6312 Konferensbidrag (refereegranskat)abstract Particle Markov Chain Monte Carlo (PMCMC) samplers allow for routine inference of parameters and states in challenging nonlinear problems. A common choice for the parameter proposal is a simple random walk sampler, which can scale poorly with the number of parameters.In this paper, we propose to use log-likelihood gradients, i.e. the score, in the construction of the proposal, akin to the Langevin Monte Carlo method, but adapted to the PMCMC framework. This can be thought of as a way to guide a random walk proposal by using drift terms that are proportional to the score function. The method is successfully applied to a stochastic volatility model and the drift term exhibits intuitive behaviour.
23.	Dahlin, Johan, 1986-, et al. (författare) Quasi-Newton particle Metropolis-Hastings 2015 Ingår i: Proceedings of the 17th IFAC Symposium on System Identification.. - : Elsevier. ; , s. 981-986 Konferensbidrag (refereegranskat)abstract Particle Metropolis-Hastings enables Bayesian parameter inference in general nonlinear state space models (SSMs). However, in many implementations a random walk proposal is used and this can result in poor mixing if not tuned correctly using tedious pilot runs. Therefore, we consider a new proposal inspired by quasi-Newton algorithms that may achieve similar (or better) mixing with less tuning. An advantage compared to other Hessian based proposals, is that it only requires estimates of the gradient of the log-posterior. A possible application is parameter inference in the challenging class of SSMs with intractable likelihoods.We exemplify this application and the benefits of the new proposal by modelling log-returns offuture contracts on coffee by a stochastic volatility model with alpha-stable observations.
24.	Dahlin, Johan, 1986-, et al. (författare) Robust ARX Models with Automatic Order Determination and Student's t Innovations 2011 Rapport (övrigt vetenskapligt/konstnärligt)abstract ARX models is a common class of models of dynamical systems. Here, we consider the case when the innovation process is not well described by Gaussian noise and instead propose to model the driving noise as Student's t distributed. The t distribution is more heavy tailed than the Gaussian distribution, which provides an increased robustness to data anomalies, such as outliers and missing observations. We use a Bayesian setting and design the models to also include an automatic order determination. Basically, this means that we infer knowledge about the posterior distribution of the model order from data. We consider two related models, one with a parametric model order and one with a sparseness prior on the ARX coefficients. We derive Markov chain Monte Carlo samplers to perform inference in these models. Finally, we provide three numerical illustrations with both simulated data and real EEG data to evaluate the proposed methods.
25.	Dahlin, Johan, 1986-, et al. (författare) Second-Order Particle MCMC for Bayesian Parameter Inference 2014 Ingår i: Proceedings of the 19th IFAC World Congress. ; , s. 8656-8661 Konferensbidrag (refereegranskat)abstract We propose an improved proposal distribution in the Particle Metropolis-Hastings (PMH) algorithm for Bayesian parameter inference in nonlinear state space models. This proposal incorporates second-order information about the parameter posterior distribution, which can be extracted from the particle filter already used within the PMH algorithm. The added information makes the proposal scale-invariant, simpler to tune and can possibly also shorten the burn-in phase. The proposed algorithm has a computational cost which is proportional to the number of particles, i.e. the same as the original marginal PMH algorithm. Finally, we provide two numerical examples that illustrates some of the possible benefits of adding the second-order information.
26.	Dahlin, Johan (författare) Sequential Monte Carlo for inference in nonlinear state space models 2014 Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract Nonlinear state space models (SSMs) are a useful class of models to describe many different kinds of systems. Some examples of its applications are to model; the volatility in financial markets, the number of infected persons during an influenza epidemic and the annual number of major earthquakes around the world. In this thesis, we are concerned with state inference, parameter inference and input design for nonlinear SSMs based on sequential Monte Carlo (SMC) methods.The state inference problem consists of estimating some latent variable that is not directly observable in the output from the system. The parameter inference problem is concerned with fitting a pre-specified model structure to the observed output from the system. In input design, we are interested in constructing an input to the system, which maximises the information that is available about the parameters in the system output. All of these problems are analytically intractable for nonlinear SSMs. Instead, we make use of SMC to approximate the solution to the state inference problem and to solve the input design problem. Furthermore, we make use of Markov chain Monte Carlo (MCMC) and Bayesian optimisation (BO) to solve the parameter inference problem.In this thesis, we propose new methods for parameter inference in SSMs using both Bayesian and maximum likelihood inference. More specifically, we propose a new proposal for the particle Metropolis-Hastings algorithm, which includes gradient and Hessian information about the target distribution. We demonstrate that the use of this proposal can reduce the length of the burn-in phase and improve the mixing of the Markov chain.Furthermore, we develop a novel parameter inference method based on the combination of BO and SMC. We demonstrate that this method requires a relatively small amount of samples from the analytically intractable likelihood, which are computationally costly to obtain. Therefore, it could be a good alternative to other optimisation based parameter inference methods. The proposed BO and SMC combination is also extended for parameter inference in nonlinear SSMs with intractable likelihoods using approximate Bayesian computations. This method is used for parameter inference in a stochastic volatility model with -stable returns using real-world financial data.Finally, we develop a novel method for input design in nonlinear SSMs which makes use of SMC methods to estimate the expected information matrix. This information is used in combination with graph theory and convex optimisation to estimate optimal inputs with amplitude constraints. We also consider parameter estimation in ARX models with Student-t innovations and unknown model orders. Two different algorithms are used for this inference: reversible Jump Markov chain Monte Carlo and Gibbs sampling with sparseness priors. These methods are used to model real-world EEG data with promising results.
27.	Ekström Kelvinius, Filip, et al. (författare) Discriminator Guidance for Autoregressive Diffusion Models 2024 Ingår i: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. - : PMLR. ; , s. 3403-3411 Konferensbidrag (refereegranskat)abstract We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuous diffusion models, and in this work we derive ways of using a discriminator together with a pretrained generative model in the discrete case. First, we show that using an optimal discriminator will correct the pretrained model and enable exact sampling from the underlying data distribution. Second, to account for the realistic scenario of using a sub-optimal discriminator, we derive a sequential Monte Carlo algorithm which iteratively takes the predictions from the discriminator into account during the generation process. We test these approaches on the task of generating molecular graphs and show how the discriminator improves the generative performance over using only the pretrained model.
28.	Ekström Kelvinius, Filip, et al. (författare) Graph-based machine learning beyond stable materials and relaxed crystal structures 2022 Ingår i: Physical Review Materials. - : American Physical Society. - 2475-9953. ; 6:3 Tidskriftsartikel (refereegranskat)abstract There has been a recent surge of interest in using machine learning to approximate density functional theory in materials science. However, many of the most performant models are evaluated on large databases of computed properties of, primarily, materials with precise atomic coordinates available, and which have been experimentally synthesized, i.e., which are thermodynamically stable or metastable. These aspects provide challenges when applying such models on theoretical candidate materials, for example for materials discovery, where the coordinates are not known. To extend the scope of this methodology, we investigate the performance of the crystal graph convolutional neural network on a data set of theoretical structures in three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many highly unstable structures. We then investigate the impact on the performance of using atomic positions that are only partially relaxed into local energy minima We also explore options for improving the performance in these scenarios by transfer learning, either from models trained on a large database of mostly stable systems, or a different but related phase diagram. Models pretrained on stable materials do not significantly improve performance, but models trained on similar data transfer very well. We demonstrate how our findings can be utilized to generate phase diagrams with a major reduction in computational effort.
29.	Frigola, Roger, et al. (författare) Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC 2013 Ingår i: Advances in Neural Information Processing Systems 26. Konferensbidrag (refereegranskat)abstract State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once an approximation of the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.
30.	Frigola, Roger, et al. (författare) Identification of Gaussian process state-space models with particle stochastic approximation EM 2014 Ingår i: Proceedings of the 19th IFAC World Congress. - : Elsevier. Konferensbidrag (refereegranskat)abstract Gaussian process state-space models (GP-SSMs) are a very exible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification.
31.	Glaser, Pierre, et al. (författare) Fast and Scalable Score-Based Kernel Calibration Tests Annan publikation (övrigt vetenskapligt/konstnärligt)abstract We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a non-parametric, kernel-based test for assessing the calibration of probabilistic models with well-defined scores. In contrast to previous methods, our test avoids the need for possibly expensive expectation approximations while providing control over its type-I error. We achieve these improvements by using a new family of kernels for score-based probabilities that can be estimated without probability density samples, and by using a conditional goodness-of-fit criterion for the KCCSD test’s U-statistic. The tractability of the KCCSD test widens the surface area of calibration measures to new promising use-cases, such as regularization during model training. We demonstrate the properties of our test on various synthetic settings.
32.	Glaser, Pierre, et al. (författare) Fast and Scalable Score-Based Kernel Calibration Tests 2023 Ingår i: Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Konferensbidrag (refereegranskat)abstract We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a non-parametric, kernel-based test for assessing the calibration of probabilistic models with well-defined scores. In contrast to previous methods, our test avoids the need for possibly expensive expectation approximations while providing control over its type-I error. We achieve these improvements by using a new family of kernels for score-based probabilities that can be estimated without probability density samples, and by using a conditional goodness-of-fit criterion for the KCCSD test’s U-statistic. The tractability of the KCCSD test widens the surface area of calibration measures to new promising use-cases, such as regularization during model training. We demonstrate the properties of our test on various synthetic settings.
33.	Govindarajan, Hariprasath, et al. (författare) DINO as a von Mises-Fisher mixture model 2023 Ingår i: The Eleventh International Conference on Learning Representations. Konferensbidrag (refereegranskat)abstract Self-distillation methods using Siamese networks are popular for self-supervised pre-training. DINO is one such method based on a cross-entropy loss between K-dimensional probability vectors, obtained by applying a softmax function to the dot product between representations and learnt prototypes. Given the fact that the learned representations are L2-normalized, we show that DINO and its derivatives, such as iBOT, can be interpreted as a mixture model of von Mises-Fisher components. With this interpretation, DINO assumes equal precision for all components when the prototypes are also L2-normalized. Using this insight we propose DINO-vMF, that adds appropriate normalization constants when computing the cluster assignment probabilities. Unlike DINO, DINO-vMF is stable also for the larger ViT-Base model with unnormalized prototypes. We show that the added flexibility of the mixture model is beneficial in terms of better image representations. The DINO-vMF pre-trained model consistently performs better than DINO on a range of downstream tasks. We obtain similar improvements for iBOT-vMF vs iBOT and thereby show the relevance of our proposed modification also for other methods derived from DINO.
34.	Govindarajan, Hariprasath, et al. (författare) Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes 2021 Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - : IEEE. - 9781665479219 - 9781665479226 ; , s. 249-256 Konferensbidrag (refereegranskat)abstract Although Content Based Image Retrieval (CBIR) is an active research field, application to images simultaneously containing multiple objects has received limited research inter- est. For such complex images, it is difficult to precisely convey the query intention, to encode all the image aspects into one compact global feature representation and to unambiguously define label similarity or dissimilarity. Motivated by the recent success on many visual benchmark tasks, we propose a self- supervised method to train a feature representation learning model. We propose usage of multiple query images, and use an attention based architecture to extract features from diverse image aspects that benefits from this. The method shows promising performance on road scene datasets, and, consistently improves when multiple query images are used instead of a single query image. © 2021 IEEE.
35.	Jacob, Pierre E., et al. (författare) Smoothing With Couplings of Conditional Particle Filters 2020 Ingår i: Journal of the American Statistical Association. - : Informa UK Limited. - 0162-1459 .- 1537-274X. ; 115:530, s. 721-729 Tidskriftsartikel (refereegranskat)abstract In state-space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka-Volterra model with an intractable transition density. Supplementary materials for this article are available online.
36.	Jacob, Pierre, et al. (författare) Retracted article: Smoothing with Couplings of Conditional Particle Filters 2018 Ingår i: Journal of the American Statistical Association. - : Taylor & Francis. - 0162-1459 .- 1537-274X. Tidskriftsartikel (refereegranskat)abstract In state space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and confidence intervals can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly-informative observations, and for a realistic Lotka-Volterra model with an intractable transition density.
37.	Konold, Patrick, et al. (författare) Microsecond time-resolved X-ray scattering by utilizing MHz repetition rate at second-generation XFELs Annan publikation (övrigt vetenskapligt/konstnärligt)abstract Detecting microsecond structural perturbations in biomolecules has wide relevance inbiology, chemistry, and medicine. Here, we show how MHz repetition rates at X-ray freeelectron lasers (XFELs) can be used to produce microsecond time-series of proteinscattering with exceptionally low noise levels of 0.001%. We demonstrate the approach byderiving new mechanistic insight into Jɑ helix unfolding of a Light-Oxygen-Voltage (LOV)photosensory domain. This time-resolved acquisition strategy is easy to implement andwidely applicable for direct observation of structural dynamics of many biochemicalprocesses.
38.	Kudlicka, Jan, et al. (författare) Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood 2020 Ingår i: ICASSP 2020. - : IEEE. - 9781509066322 - 9781509066315 ; , s. 5860-5864 Konferensbidrag (refereegranskat)abstract We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter. Being able to estimate the marginal likelihood without bias is highly relevant for model comparison, computation of interpretable and reliable confidence intervals, and in exact approximation methods, such as particle Markov chain Monte Carlo. In the paper we present a particle filter with rejection control that enables unbiased estimation of the marginal likelihood.
39.	Lacoste-Julien, Simon, et al. (författare) Sequential Kernel Herding : Frank-Wolfe Optimization for Particle Filtering 2015 Ingår i: Proceedings of the 18th International Conference on Artificial Intelligence and Statistics. Konferensbidrag (refereegranskat)
40.	Lindholm, Andreas, et al. (författare) Machine learning : a first course for engineers and scientists 2022 Bok (övrigt vetenskapligt/konstnärligt)abstract This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning
41.	Lindqvist, Jakob, 1992, et al. (författare) A General Framework for Ensemble Distribution Distillation 2020 Ingår i: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). - : IEEE. - 9781728166629 ; 2020-September Konferensbidrag (refereegranskat)abstract Ensembles of neural networks have shown to give better predictive performance and more reliable uncertainty estimates than individual networks. Additionally, ensembles allow the uncertainty to be decomposed into aleatoric (data) and epistemic (model) components, giving a more complete picture of the predictive uncertainty. Ensemble distillation is the process of compressing an ensemble into a single model, often resulting in a leaner model that still outperforms the individual ensemble members. Unfortunately, standard distillation erases the natural uncertainty decomposition of the ensemble. We present a general framework for distilling both regression and classification ensembles in a way that preserves the decomposition. We demonstrate the desired behaviour of our framework and show that its predictive performance is on par with standard distillation.
42.	Lindsten, Fredrik, et al. (författare) A non-degenerate rao-blackwellised particle filter for estimating static parameters in dynamical models 2012 Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - 2405-8963. - 9783902823069 ; 16:1, s. 1149-1154 Konferensbidrag (refereegranskat)abstract The particle filter (PF) has emerged as a powerful tool for solving nonlinear and/or non-Gaussian filtering problems. When some of the states enter the model linearly, this can be exploited by using particles only for the "nonlinear" states and employing conditional Kalman filters for the "linear" states; this leads to the Rao-Blackwellised particle filter (RBPF). However, it is well known that the PF fails when the state of the model contains some static parameter. This is true also for the RBPF, even if the static states are marginalised analytically by a Kalman filter. The reason is that the posterior density of the static states is computed conditioned on the nonlinear particle trajectories, which are bound to degenerate over time. To circumvent this problem, we propose a method for targeting the posterior parameter density, conditioned on just the current nonlinear state. This results in an RBPF-like method, capable of recursive identification of nonlinear dynamical models with affine parameter dependencies.
43.	Lindsten, Fredrik, et al. (författare) A Semiparametric Bayesian Approach to Wiener System Identification 2011 Rapport (övrigt vetenskapligt/konstnärligt)abstract We consider a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a state-space model for the linear dynamical system and a nonparametric Gaussian process (GP) model for the static nonlinearity. The GP model is a flexible model that can describe different types of nonlinearities while avoiding making strong assumptions such as monotonicity. We derive an inferential method based on recent advances in Monte Carlo statistical methods, known as Particle Markov Chain Monte Carlo (PMCMC). The idea underlying PMCMC is to use a particle filter (PF) to generate a sample state trajectory in a Markov chain Monte Carlo sampler. We use a recently proposed PMCMC sampler, denoted particle Gibbs with backward simulation, which has been shown to be efficient even when we use very few particles in the PF. The resulting method is used in a simulation study to identify two different Wiener systems with non-invertible nonlinearities.
44.	Lindsten, Fredrik, et al. (författare) A Semiparametric Bayesian Approach to Wiener System Identification 2012 Ingår i: Proceedings of the 16th IFAC Symposium on System Identification. - 9783902823069 ; , s. 1137-1142 Konferensbidrag (refereegranskat)abstract We consider a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a state-space model for the linear dynamical system and a nonparametric Gaussian process (GP) model for the static nonlinearity. The GP model is a flexible model that can describe different types of nonlinearities while avoiding making strong assumptions such as monotonicity. We derive an inferential method based on recent advances in Monte Carlo statistical methods, known as Particle Markov Chain Monte Carlo (PMCMC). The idea underlying PMCMC is to use a particle filter (PF) to generate a sample state trajectory in a Markov chain Monte Carlo sampler. We use a recently proposed PMCMC sampler, denoted particle Gibbs with backward simulation, which has been shown to be efficient even when we use very few particles in the PF. The resulting method is used in a simulation study to identify two different Wiener systems with non-invertible nonlinearities.
45.	Lindsten, Fredrik (författare) An Efficient Stochastic Approximation EM Algorithm using Conditional Particle Filters 2013 Ingår i: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing. - : IEEE conference proceedings. ; , s. 6274-6278 Konferensbidrag (refereegranskat)abstract I present a novel method for maximum likelihood parameter estimation in nonlinear/non-Gaussian state-space models. It is an expectation maximization (EM) like method, which uses sequential Monte Carlo (SMC) for the intermediate state inference problem. Contrary to existing SMC-based EM algorithms, however, it makes efficient use of the simulated particles through the use of particle Markov chain Monte Carlo (PMCMC) theory. More precisely, the proposed method combines the efficient conditional particle filter with ancestor sampling (CPF-AS) with the stochastic approximation EM (SAEM) algorithm. This results in a procedure which does not rely on asymptotics in the number of particles for convergence, meaning that the method is very computationally competitive. Indeed, the method is evaluated in a simulation study, using a small number of particles with promising results.
46.	Lindsten, Fredrik, 1984-, et al. (författare) An Explicit Variance Reduction Expression for the Rao-Blackwellised Particle Filter 2010 Rapport (övrigt vetenskapligt/konstnärligt)abstract Particle filters (PFs) have shown to be very potent tools for state estimation in nonlinear and/or non-Gaussian state-space models. For certain models, containing a conditionally tractable substructure (typically conditionally linear Gaussian or with finite support), it is possible to exploit this structure in order to obtain more accurate estimates. This has become known as Rao-Blackwellised particle filtering (RBPF). However, since the RBPF is typically more computationally demanding than the standard PF per particle, it is not always beneficial to resort to Rao-Blackwellisation. For the same computational effort, a standard PF with an increased number of particles, which would also increase the accuracy, could be used instead. In this paper, we have analysed the asymptotic variance of the RBPF and provide an explicit expression for the obtained variance reduction. This expression could be used to make an efficient discrimination of when to apply Rao-Blackwellisation, and when not to.
47.	Lindsten, Fredrik, et al. (författare) An Explicit Variance Reduction Expression for the Rao-Blackwellised Particle Filter 2011 Ingår i: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 11979-11984 Konferensbidrag (refereegranskat)abstract Particle filters (PFs) have shown to be very potent tools for state estimation in nonlinear and/or non-Gaussian state-space models. For certain models, containing a conditionally tractable substructure (typically conditionally linear Gaussian or with finite support), it is possible to exploit this structure in order to obtain more accurate estimates. This has become known as Rao-Blackwellised particle filtering (RBPF). However, since the RBPF is typically more computationally demanding than the standard PF per particle, it is not always beneficial to resort to Rao-Blackwellisation. For the same computational effort, a standard PF with an increased number of particles, which would also increase the accuracy, could be used instead. In this paper, we have analysed the asymptotic variance of the RBPF and provide an explicit expression for the obtained variance reduction. This expression could be used to make an efficient discrimination of when to apply Rao-Blackwellisation, and when not to.
48.	Lindsten, Fredrik, et al. (författare) Ancestor Sampling for Particle Gibbs 2012 Ingår i: Proceedings of the 26th Conference on Neural Information Processing Systems. - 9781627480031 Konferensbidrag (refereegranskat)abstract We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PG-AS). Similarly to the existing PG with backward simulation (PG-BS) procedure, we use backward sampling to (considerably) improve the mixing of the PG kernel. Instead of using separate forward and backward sweeps as in PG-BS, however, we achieve the same effect in a single forward sweep. We apply the PG-AS framework to the challenging class of non-Markovian state-space models. We develop a truncation strategy of these models that is applicable in principle to any backward-simulation-based method, but which is particularly well suited to the PG-AS framework. In particular, as we show in a simulation study, PG-AS can yield an order-of-magnitude improved accuracy relative to PG-BS due to its robustness to the truncation error. Several application examples are discussed, including Rao-Blackwellized particle smoothing and inference in degenerate state-space models.
49.	Lindsten, Fredrik, et al. (författare) Backward simulation methods for Monte Carlo statistical inference 2013 Ingår i: Foundations and Trends in Machine Learning. - : Now Publishers. - 1935-8237 .- 1935-8245. ; 6:1, s. 1-143 Tidskriftsartikel (refereegranskat)abstract Monte Carlo methods, in particular those based on Markov chains and on interacting particle systems, are by now tools that are routinely used in machine learning. These methods have had a profound impact on statistical inference in a wide range of application areas where probabilistic models are used. Moreover, there are many algorithms in machine learning which are based on the idea of processing the data sequentially, first in the forward direction and then in the backward direction. In this tutorial we will review a branch of Monte Carlo methods based on the forward-backward idea, referred to as backward simulators. These methods are useful for learning and inference in probabilistic models containing latent stochastic processes. The theory and practice of backward simulation algorithms have undergone a significant development in recent years and the algorithms keep finding new applications. The foundation for these methods is sequential Monte Carlo (SMC). SMC-based backward simulators are capable of addressing smoothing problems in sequential latent variable models, such as general, nonlinear/non-Gaussian state-space models (SSMs). However, we will also clearly show that the underlying backward simulation idea is by no means restricted to SSMs. Furthermore, backward simulation plays an important role in recent developments of Markov chain Monte Carlo (MCMC) methods. Particle MCMC is a systematic way of using SMC within MCMC. In this framework, backward simulation gives us a way to significantly improve the performance of the samplers. We review and discuss several related backward-simulation-based methods for state inference as well as learning of static parameters, both using a frequentistic and a Bayesian approach.
50.	Lindsten, Fredrik, et al. (författare) Bayesian semiparametric Wiener system identification 2013 Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 49:7, s. 2053-2063 Tidskriftsartikel (refereegranskat)abstract We present a novel method for Wiener system identification. The method relies on a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a state-space model for the linear dynamical system and a nonparametric Gaussian process model for the static nonlinearity. We avoid making strong assumptions, such as monotonicity, on the nonlinear mapping. Stochastic disturbances, entering both as measurement noise and as process noise, are handled in a systematic manner. The nonparametric nature of the Gaussian process allows us to handle a wide range of nonlinearities without making problem-specific parameterizations. We also consider sparsity-promoting priors, based on generalized hyperbolic distributions, to automatically infer the order of the underlying dynamical system. We derive an inference algorithm based on an efficient particle Markov chain Monte Carlo method, referred to as particle Gibbs with ancestor sampling. The method is profiled on two challenging identification problems with good results. Blind Wiener system identification is handled as a special case.

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