PUMA publications for /user/folke/bayesianTue May 04 08:55:46 CEST 2010cite arxiv:0805.2689
Comment: 12 pages, 5 figures. New sections addedBayesian approach to clustering real value, categorical and network
data: solution via variational methods2008bayesian clustering community detection Data clustering, including problems such as finding network communities, can
be put into a systematic framework by means of a Bayesian approach. The
application of Bayesian approaches to real problems can be, however, quite
challenging. In most cases the solution is explored via Monte Carlo sampling or
variational methods. Here we work further on the application of variational
methods to clustering problems. We introduce generative models based on a
hidden group structure and prior distributions. We extend previous attends by
Jaynes, and derive the prior distributions based on symmetry arguments. As a
case study we address the problems of two-sides clustering real value data and
clustering data represented by a hypergraph or bipartite graph. From the
variational calculations, and depending on the starting statistical model for
the data, we derive a variational Bayes algorithm, a generalized version of the
expectation maximization algorithm with a built in penalization for model
complexity or bias. We demonstrate the good performance of the variational
Bayes algorithm using test examples.
Tue May 04 08:55:46 CEST 2010Web: http://www. arbylon. net/publications/text-est. pdf{Parameter estimation for text analysis}2005bayesian introduction lda tutorial Tue May 04 08:55:46 CEST 2010Machine Learning and Knowledge Discovery in Databases522--537Latent Dirichlet Bayesian Co-Clustering20092009 bayesian clustering ecml lda pkdd Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing co-clustering
algorithms are hard partitioning, assigning each row and column of the data matrix to one cluster. Recently a Bayesian co-clusteringapproach has been proposed which allows a probability distribution membership in row and column clusters. The approach usesvariational inference for parameter estimation. In this work, we modify the Bayesian co-clustering model, and use collapsedGibbs sampling and collapsed variational inference for parameter estimation. Our empirical evaluation on real data sets showsthat both collapsed Gibbs sampling and collapsed variational inference are able to find more accurate likelihood estimatesthan the standard variational Bayesian co-clustering approach.Tue May 04 08:55:46 CEST 2010ICDMconf/icdm/2008530-539Bayesian Co-clustering.2008bayesian clustering co-clustering lda Tue May 04 08:55:46 CEST 2010Journal of Machine Learning Research1705-1749Clustering with Bregman Divergences.62005bayesian clustering lda mixture models Tue May 04 08:55:46 CEST 2010Hingham, MA, USAMach. Learn.2183--233An Introduction to Variational Methods for Graphical Models371999bayesian graphical models variational Tue May 04 08:55:46 CEST 2010Learning in Graphical Models1998bayesian graphical learning ml model Tue May 04 08:55:46 CEST 2010Learning in graphical models9--26{Introduction to inference for Bayesian networks}1999bayesian inference networks Tue May 04 08:55:46 CEST 2010{Advanced inference in Bayesian networks}1998bayesian introduction ml networks Tue May 04 08:55:46 CEST 2010IEEE Trans. Pattern Anal. Machine Intell721--741{Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images}61984bayesian distribution estimation gibbs parameter sampling Tue May 04 08:55:46 CEST 2010Advances in Neural Information Processing SystemsCollapsed Variational Inference for {HDP}202008bayesian collapsed hierachical inference lda variational Tue May 04 08:55:46 CEST 2010Journal of Artificial Intelligence Research159--225Operations for Learning with Graphical Models21994bayesian graphical introduction ml models notation plate This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...Tue May 04 08:55:46 CEST 2010WebAn introduction to graphical models2001bayesian graphical introduction models Tue May 04 08:55:46 CEST 2010New York, NY, USASIGGRAPH '04: ACM SIGGRAPH 2004 Course Notes22Introduction to Bayesian learning2004bayesian introduction learning Sophisticated computer graphics applications require complex models of appearance, motion, natural phenomena, and even artistic style. Such models are often difficult or impossible to design by hand. Recent research demonstrates that, instead, we can "learn" a dynamical and/or appearance model from captured data, and then synthesize realistic new data from the model. For example, we can capture the motions of a human actor and then generate new motions as they might be performed by that actor. Bayesian reasoning is a fundamental tool of machine learning and statistics, and it provides powerful tools for solving otherwise-difficult problems of learning about the world from data. Beginning from first principles, this course develops the general methodologies for designing learning algorithms and describes their application to several problems in graphics.Tue May 04 08:55:46 CEST 2010{Learning in graphical models}1998bayesian graphical introduction models Tue May 04 08:55:46 CEST 2010Journal of the American Statistical Association4761566--1581{Hierarchical dirichlet processes}1012006bayesian dirichlet gibbs hierarchical lda