Bayesian Modeling and Classification of Neural Signals

Signal processing and classification algorithms often have limited applicability resulting from an inaccurate model of the signal's underlying structure. We present here an efficient, Bayesian algorithm for modeling a signal composed of the superposition of brief, Poisson-distributed functions. This methodology is applied to the specific problem of modeling and classifying extracellular neural waveforms which are composed of a superposition of an unknown number of action potentials CAPs). Previous approaches have had limited success due largely to the problems of determining the spike shapes, deciding how many are shapes distinct, and decomposing overlapping APs. A Bayesian solution to each of these problems is obtained by inferring a probabilistic model of the waveform. This approach quantifies the uncertainty of the form and number of the inferred AP shapes and is used to obtain an efficient method for decomposing complex overlaps. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits

Bayesian Modeling of a Human MMORPG Player

This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.Comment: 30th international workshop on Bayesian Inference and Maximum Entropy, Chamonix : France (2010

Non-parametric Bayesian modeling of complex networks

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature

Bayesian modeling for composite reliability and maximal reliability.

A reliability coefficient in psychometrics is used as an index of consistency. The α coefficient has been widely used as an estimate of reliability coefficient: however, in recent years, there has been an increasing interest in devising other methods of estimating reliability. I have made extensive revisions to enhance clarity and reduce redundancy. In addition to reporting the point estimate of the reliability coefficient, it is also recommended to report the results of interval estimation. Furthermore, psychological research using Bayesian modeling is gradually gaining popularity. In this paper, we introduce a Bayesian model for obtaining the point and interval estimation of maximal reliability and ω coefficient using a statistical analysis environment R and Stan that implements HMC sampling.信頼性係数は心理尺度開発場面で、尺度の安定の度合いを示す指標として利用されている。信頼性係数の代表的な指標としてα係数が広く利用されてきた。近年、α係数の再検討が進み、その他の信頼性係数の指標にも関心が高まっている。また、信頼性係数の報告も点推定値のみならず、区間推定を行った結果を報告する事も意識されるようになっている。更に、ベイズモデリングを利用した心理学研究が増えつつある。本稿では統計解析環境RおよびHMCサンプリングを実装したStanを用いて、ベイズモデリングによって最大信頼性およびω係数の推定値と確信区間を構成する方法を紹介する

Bayesian modeling of recombination events in bacterial populations

Background: We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases. Results: We introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites. Conclusion: A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus Burkholderia are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL http://web.abo.fi/fak/ mnf//mate/jc/software/brat.html

Hierarchical Bayesian Modeling of Hitting Performance in Baseball

We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm provides a principled method for balancing past performance with crucial covariates, such as player age and position. We share information across time and across players by using mixture distributions to control shrinkage for improved accuracy. We compare the performance of our model to current sabermetric methods on a held-out season (2006), and discuss both successes and limitations

Nonparametric Bayesian Modeling for Automated Database Schema Matching

The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models