| 1. |
- Armerin, Fredrik, 1971-, et al.
(författare)
-
Forecasting Ranking in Harness Racing Using Probabilities Induced by Expected Positions
- 2019
-
Ingår i: Applied Artificial Intelligence. - : TAYLOR & FRANCIS INC. - 0883-9514 .- 1087-6545. ; 33:2, s. 171-189
-
Tidskriftsartikel (refereegranskat)abstract
- Ranked events are pivotal in many important AI-applications such as Question Answering and recommendations systems. This paper studies ranked events in the setting of harness racing. For each horse there exists a probability distribution over its possible rankings. In the paper, it is shown that a set of expected positions (and more generally, higher moments) for the horses induces this probability distribution. The main contribution of the paper is a method, which extracts this induced probability distribution from a set of expected positions. An algorithm is proposed where the extraction of the induced distribution is given by the estimated expectations. MATLAB code is provided for the methodology. This approach gives freedom to model the horses in many different ways without the restrictions imposed by for instance logistic regression. To illustrate this point, we employ a neural network and ordinary ridge regression. The method is applied to predicting the distribution of the finishing positions for horses in harness racing. It outperforms both multinomial logistic regression and the market odds. The ease of use combined with fine results from the suggested approach constitutes a relevant addition to the increasingly important field of ranked events.
|
|
| 2. |
- Austin, Brian, et al.
(författare)
-
Sliding window discretization : A new method for multiple band matching of bacterial genotyping fingerprints
- 2004
-
Ingår i: Bulletin of Mathematical Biology. - : Springer Science and Business Media LLC. - 0092-8240 .- 1522-9602. ; 66:6, s. 1575-1596
-
Tidskriftsartikel (refereegranskat)abstract
- Microbiologists have traditionally applied hierarchical clustering algorithms as their mathematical tool of choice to unravel the taxonomic relationships between micro-organisms. However, the interpretation of such hierarchical classifications suffers from being subjective, in that a variety of ad hoc choices must be made during their construction. On the other hand, the application of more profound and objective mathematical methods - such as the minimization of stochastic complexity - for the classification of bacterial genotyping fingerprints data is hampered by the prerequisite that such methods only act upon vectorized data. In this paper we introduce a new method, coined sliding window discretization, for the transformation of genotypic fingerprint patterns into binary vector format. In the context of an extensive amplified fragment length polymorphism (AFLP) data set of 507 strains from the Vibrionaceae family that has previously been analysed, we demonstrate by comparison with a number of other discretization methods that this new discretization method results in minimal loss of the original information content captured in the banding patterns. Finally, we investigate the implications of the different discretization methods on the classification of bacterial genotyping fingerprints by minimization of stochastic complexity, as it is implemented in the BinClass software package for probabilistic clustering of binary vectors. The new taxonomic insights learned from the resulting classification of the AFLP patterns will prove the value of combining sliding window discretization with minimization of stochastic complexity, as an alternative classification algorithm for bacterial genotyping fingerprints.
|
|
| 3. |
- Berglund, Daniel, et al.
(författare)
-
Measures of Additive Interactionand Effect Direction
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Measures for additive interaction are defined using risk ratios. These ratios need to be modeled so that all combinations of the exposures are harmful, as the scale between protective and harmful factors differs. This remodeling is referred to as recoding. Previously, recoding has been thought of as random. In this paper, we will examine and discuss the impact of recoding in studies with small effect sizes, such as genome wide association studies, and the impact recoding has on significance testing.
|
|
| 4. |
- Berglund, Daniel, et al.
(författare)
-
On Probabilistic Multifactor Potential Outcome Models
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The sufficient cause framework describes how sets of sufficient causes are responsible for causing some event or outcome. It is known that it is closely connected with Boolean functions. In this paper we define this relation formally, and show how it can be used together with Fourier expansion of the Boolean functions to lead to new insights. The main result is a probibalistic version of the multifactor potential outcome model based on independence of causal influence models and Bayesian networks.
|
|
| 5. |
- Berglund, Daniel, et al.
(författare)
-
On the Existence of Suitable Models for Additive Interaction with Continuous Exposures
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Additive interaction can be of importance for public health interventions and it is commonly defined using binary exposures. There has been expansions of the models to also include continuous exposures, which could lead to better and more precise estimations of the effect of interventions. In this paper we define the intervention for a continuous exposure as a monotonic function. Based on this function for the interventions we prove that there is no model for estimating additive interactions with continuous exposures for which it holds that; (i) both exposures have marginal effects and no additive interaction on the exposure level for both exposures, (ii) neither exposure has marginal effect and there is additive interaction between the exposures. We also show that a logistic regression model for continuous exposures will always produce additive interaction if both exposures have marginal effects.
|
|
| 6. |
- Corander, Jukka, et al.
(författare)
-
Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy
- 2009
-
Ingår i: Advances in Data Analysis and Classification. - : Springer Berlin/Heidelberg. - 1862-5347 .- 1862-5355. ; 3:1, s. 3-24
-
Tidskriftsartikel (refereegranskat)abstract
- Advantages of statistical model-based unsupervised classification over heuristic alternatives have been widely demonstrated in the scientific literature. However, the existing model-based approaches are often both conceptually and numerically instable for large and complex data sets. Here we consider a Bayesian model-based method for unsupervised classification of discrete valued vectors, that has certain advantages over standard solutions based on latent class models. Our theoretical formulation defines a posterior probability measure on the space of classification solutions corresponding to stochastic partitions of observed data. To efficiently explore the classification space we use a parallel search strategy based on non-reversible stochastic processes. A decision-theoretic approach is utilized to formalize the inferential process in the context of unsupervised classification. Both real and simulated data sets are used for the illustration of the discussed methods.
|
|
| 7. |
- Corander, Jukka, et al.
(författare)
-
Bayesian Unsupervised Learning of DNA Regulatory Binding Regions
- 2009
-
Ingår i: Advances in Artificial Intelligence. - : Hindawi Publishing Corporation. - 1687-7470 .- 1687-7489. ; , s. 219743-
-
Tidskriftsartikel (refereegranskat)abstract
- Identification of regulatory binding motifs, that is, short specific words, within DNA sequences is a commonly occurring problem in computational bioinformatics. A wide variety of probabilistic approaches have been proposed in the literature to either scan for previously known motif types or to attempt de novo identification of a fixed number (typically one) of putative motifs. Mostapproaches assume the existence of reliable biodatabase information to build probabilistic a priori description of the motif classes. Examples of attempts to do probabilistic unsupervised learning about the number of putative de novo motif types and theirpositions within a set of DNA sequences are very rare in the literature. Here we show how such a learning problem can be formulated using a Bayesian model that targets to simultaneously maximize the marginal likelihood of sequence data arising under multiple motif types as well as under the background DNA model, which equals a variable length Markov chain. It is demonstrated how the adopted Bayesian modelling strategy combined with recently introduced nonstandard stochastic computation tools yields a more tractable learning procedure than is possible with the standard Monte Carlo approaches. Improvements and extensions of the proposed approach are also discussed.
|
|
| 8. |
- Corander, Jukka, 1965-, et al.
(författare)
-
Have I seen you before? : Principles of Bayesian predictive classification revisited
- 2013
-
Ingår i: Statistics and computing. - : Springer Berlin/Heidelberg. - 0960-3174 .- 1573-1375. ; 23:1, s. 59-73
-
Tidskriftsartikel (refereegranskat)abstract
- A general inductive Bayesian classification framework is considered using a simultaneous predictive distribution for test items. We introduce a principle of generative supervised and semi-supervised classification based on marginalizing the joint posterior distribution of labels for all test items. The simultaneous and marginalized classifiers arise under different loss functions, while both acknowledge jointly all uncertainty about the labels of test items and the generating probability measures of the classes. We illustrate for data from multiple finite alphabets that such classifiers achieve higher correct classification rates than a standard marginal predictive classifier which labels all test items independently, when training data are sparse. In the supervised case for multiple finite alphabets the simultaneous and the marginal classifiers are proven to become equal under generalized exchangeability when the amount of training data increases. Hence, the marginal classifier can be interpreted as an asymptotic approximation to the simultaneous classifier for finite sets of training data. It is also shown that such convergence is not guaranteed in the semi-supervised setting, where the marginal classifier does not provide a consistent approximation.
|
|
| 9. |
- Dawyndt, Peter, et al.
(författare)
-
A complementary approach to systematics
- 2005
-
Ingår i: Microbiology Today. - 1464-0570. ; :February, s. 38-38
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 10. |
- Ekdahl, Magnus, 1979-, et al.
(författare)
-
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers
- 2007
-
Ingår i: Machine Learning and Data Mining in Pattern Recognition. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540734987 - 9783540734994 - 3540734988 ; , s. 2-16
-
Bokkapitel (refereegranskat)abstract
- Computational procedures using independence assumptions in various forms are popular in machine learning, although checks on empirical data have given inconclusive results about their impact. Some theoretical understanding of when they work is available, but a definite answer seems to be lacking. This paper derives distributions that maximizes the statewise difference to the respective product of marginals. These distributions are, in a sense the worst distribution for predicting an outcome of the data generating mechanism by independence. We also restrict the scope of new theoretical results by showing explicitly that, depending on context, independent ('Naïve') classifiers can be as bad as tossing coins. Regardless of this, independence may beat the generating model in learning supervised classification and we explicitly provide one such scenario.
|
|
