| 1. |
- Aihara, ShinIchi, et al.
(author)
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Filtering for Stochastic Volatility by Using Exact Sampling and Application to Term Structure Modeling
- 2015
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In: INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS. - Cham : Springer Science Business Media. - 9783319108919 - 9783319108902 ; , s. 329-348
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Conference paper (peer-reviewed)abstract
- The Bates stochastic volatility model is widely used in the finance problem and the sequential parameter estimation problem becomes important. By using the exact simulation technique, a particle filter for estimating stochastic volatility is constructed. The system parameters are sequentially estimated with the aid of parallel filtering algorithm with the new resampling procedure. The proposed filtering procedure is also applied to the modeling of the term structure dynamics. Simulation studies for checking the feasibility of the developed scheme are demonstrated.
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| 2. |
- Aihara, Shin Ichi, et al.
(author)
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Adaptive Filtering for Stochastic Volatility by Using Exact Sampling
- 2013
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In: 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2013). - : SciTePress - Science and and Technology Publications. - 9789898565709 ; , s. 326-335
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Conference paper (peer-reviewed)abstract
- We study the sequential identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility is constructed. The systems parameters are sequentially estimated with the aid of parallel filtering algorithm. To improve the estimation performance for unknown parameters, the new resampling procedure is proposed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.
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| 3. |
- Aihara, ShinIchi, et al.
(author)
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Identification of Bates Stochastic Volatility Model by Using Non-Central Chi-Square Random Generation Method
- 2012
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In: Proceedings of the 37th IEEE International Conference on Acoustics, Speech, and Signal Processing. - 9781467300445 - 9781467300452 ; , s. 3905-3908
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Conference paper (peer-reviewed)abstract
- We study the identification problem for Bates stochastic volatility model, which is widely used as the model of a stock in finance. By using the exact simulation method, a particle filter for estimating stochastic volatility and its systems parameters is constructed. Simulation studies for checking the feasibility of the developed scheme are demonstrated.
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| 4. |
- Fritsche, Carsten, et al.
(author)
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Bayesian Cramer-Rao Bound for Nonlinear Filtering with Dependent Noise Processes
- 2013
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In: 16th International Conference on Information Fusion (FUSION 2013). - : IEEE. - 9786058631113 ; , s. 797-804
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Conference paper (peer-reviewed)abstract
- The Bayesian Cramer Rao Bound (BCRB) is derived for nonlinear state space models with dependent process and measurement noise processes. It generalizes the previously BCRB for the case of dependent noise. Two different dependence structures appearing in literature are considered, leading to two different recursions for BCRB. The special cases of Gaussian noise, and linear models are presented separately. Simulations demonstrate that correct treatment of dependencies is important for both filtering algorithms and the BCRB.
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| 5. |
- Gustafsson, Fredrik, et al.
(author)
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Non-Linear Filtering based on Observations from Gaussian Processes
- 2011
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In: Proceedings of the 2011 IEEE Aerospace Conference. - 9781424473502
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Conference paper (peer-reviewed)abstract
- We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a finite-dimensional filter with arbitrarily good accuracy. The validity of the conceptual solution is proved via simulations on a one dimensional tracking problem and implementation issues are discussed.
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| 6. |
- Gustafsson, Fredrik, et al.
(author)
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Particle Filtering with Dependent Noise
- 2010
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In: Proceedings of the 13th Conference on Information Fusion. - 9780982443811
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Conference paper (peer-reviewed)abstract
- The theory and applications of the particle filter (PF) have developed tremendously during the past two decades. However, there appear to be no version of the PF readily applicable to the case of dependent process and measurement noise. This is in contrast to the Kalman filter, where the case of correlated noise is a standard modification. Further, the fact that sampling continuous time models give dependent noise processes is an often neglected fact in literature. We derive the optimal proposal distribution in the PF for general and Gaussian noise processes, respectively. The main result is a modified prediction step. It is demonstrated that the original Bootstrap particle filter gets a particular simple and explicit form for dependent Gaussian noise. Finally, the practical importance of dependent noise is motivated in terms of sampling of continuous time models.
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| 7. |
- Gustafsson, Fredrik, et al.
(author)
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The Benefits of Down-Sampling in the Particle Filter
- 2011
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In: Proceedings of the 14th International Conference on Information Fusion. - 9781457702679
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Conference paper (peer-reviewed)abstract
- The choice of proposal distribution in the particle filter is one of the most important design choices, and also one of the trickiest one to implement. There are basically three main options: the prior, the likelihood and the optimal proposal that combines the prior and the likelihood. The optimal proposal however, can not be obtained in most cases. The prior proposal is although easy to implement, it does not incorporate the information available otherwise from the recent observation. The prior may thus work fine for low signal to noise ratio (SNR), where the recent observation does not carry much information. However, defining the critical value of the SNR is not that obvious. On the other hand, the likelihood as a proposal always includes the information from the recent observation, but it requires that the measurement dimension is at least equal to the state dimension. We here formalize the problem, and point out an approach based on down-sampling the model. One main advantage of down-sampling is that it can decrease the problem of particle degeneracy.
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| 8. |
- Mukherjee, Saikat, et al.
(author)
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Administration of soluble gp130Fc disrupts M-1 macrophage polarization, dendritic cell activation, MDSC expansion and Th-17 induction during experimental cerebral malaria
- 2023
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In: International Immunopharmacology. - : ELSEVIER. - 1567-5769 .- 1878-1705. ; 123
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Journal article (peer-reviewed)abstract
- Regulatory effect of IL-6 on various immune cells plays a crucial role during experimental cerebral malaria pathogenesis. IL-6 neutralization can restore distorted ratios of myeloid dendritic cells and plasmacytoid dendritic cells as well as the balance between Th-17 and T-regulatory cells. IL-6 can also influence immune cells through classical and trans IL-6 signalling pathways. As trans IL-6 signalling is reportedly involved during malaria pathogenesis, we focused on studying the effects of trans IL-6 signalling blockade on various immune cell populations and how they regulate ECM progression. Results show that administration of sgp130Fc recombinant chimera protein lowers the parasitemia, increases the survivability of Plasmodium berghei ANKA infected mice, and restores the distorted ratios of M1/M2 macrophage, mDC/pDC, and Th-17/Treg. IL-6 trans signalling blockade has been found to affect both expansion of myeloid derived suppressor cells (MDSCs) and expression of inflammatory markers on them during Plasmodium berghei ANKA infection indicating that trans IL-6 signalling might regulate various immune cells and their function during ECM. In this work for the first time, we delineate the effect of sgp130Fc administration on influencing the immunological changes within the host secondary lymphoid organ during ECM induced by Plasmodium berghei ANKA infection.
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| 9. |
- Saha, Saikat
(author)
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Bayesian calibration of the Schwartz-Smith Model adapted to the energy market
- 2014
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In: 2014 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP). - : IEEE. ; , s. 508-511
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Conference paper (peer-reviewed)abstract
- We consider an application of Bayesian signal processing to the energy trading problem. In particular, we address the problem of calibrating the Schwartz-Smith Model using the observed electricity futures prices traded on the markets. As compared with the other financial markets, basic electricity derivatives such as futures are more complicated, as these products are based not on the spot prices themselves but on the arithmetic averages of the spot prices during the delivery period. As a result, the (log) futures prices are no longer affine function of the model factors and as such, an approach based on Kalman filtering, to estimate the latent model factors and the parameters seems meaningless. Here, we envisage a Bayesian approach using the particle marginal Metropolis Hastings (PMMH) algorithm for this challenging estimation task. We demonstrate the efficacy of our approach on simulated data.
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| 10. |
- Saha, Saikat, et al.
(author)
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Importance Sampling Applied to Pincus Maximization for Particle Filter MAP Estimation
- 2012
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In: 15th International Conference on Information Fusion (FUSION), 2012, Proceeding. - : International Society of Information Fusion (ISIF). - 9780982443842 - 9781467300445 ; , s. 114-120
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Conference paper (peer-reviewed)abstract
- Sequential Monte Carlo (SMC), or Particle Filters(PF), approximate the posterior distribution in nonlinear filteringarbitrarily well, but the problem how to compute a state estimateis not always straightforward. For multimodal posteriors, themaximum a posteriori (MAP) estimate is a logical choice, butit is not readily available from the SMC output. In principle,the MAP can be obtained by maximizing the posterior density obtained e.g. by the particle based approximation of theChapman-Kolmogorov equation. However, this posterior is amixture distribution with many local maxima, which makes theoptimization problem very hard. We suggest an algorithm forestimating the MAP using the global optimization principle ofPincus and subsequently outline the frameworks for estimatingthe filter and marginal smoother MAP of a dynamical systemfrom the SMC output.
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