Bayesian Analysis Toolkit: 1.0 and beyond

BAT - The Bayesian analysis toolkit

We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes’ Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution. Parameter estimation, limit setting and uncertainty propagation are implemented in a straightforward manner. A goodness-of-fit criterion is presented which is i...

Improving Information Retrieval with Textual Analysis: Bayesian Models and Beyond

Information retrieval (IR) is a difficult problem. While many have attempted to model text documents and improve search results by doing so, the most successful text retrieval to date has been developed in an ad-hoc manner. One possible reason for this is that in developing these models very little focus has been placed on the actual properties of text. In this thesis, we discuss a principled B...

PAC-Bayesian Analysis of Co-clustering and Beyond

We derive PAC-Bayesian generalization bounds for supervised and unsupervised learning models based on clustering, such as co-clustering, matrix tri-factorization, graphical models, graph clustering, and pairwise clustering.1 We begin with the analysis of co-clustering, which is a widely used approach to the analysis of data matrices. We distinguish among two tasks in matrix data analysis: discr...

Fault Tree Analysis: 10 Prime Implicants and Beyond

The performances of almost all available fault tree analysis tools are limited by the performance of the prime implicant computation procedure they use. All these procedures manipulate the prime implicants of the fault trees explicitly, so that their complexities are directly related to the number of prime implicants to be generated. This paper presents a new analysis method of coherent as well...

10 / 2007 Bayesian analysis and Markov chain Monte