Matthew J. Beal - Publications by Area

Papers by year: | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 |

Papers by area (rarely updated):

Recent additions

Beal, M.J. and Ghahramani, Z.
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
To appear, Bayesian Analysis journal, 2005
[preprint]
Teh, Y.W., Jordan, M.I., Beal, M.J. and Blei, D.M.
Hierarchical Dirichlet Processes
To appear, Journal of the American Statistical Association, 2005
[preprint]
(Older version) Technical Report 653, UC Berkeley Statistics, 2004.
[ps] [pdf] [ps.gz] [pdf.gz] [djvu] [bibtex] project page
Beal, M.J., Falciani, F., Ghahramani Z., Rangel, C. and Wild, D.L.
A Bayesian Approach to Reconstructing Genetic Regulatory Networks with Hidden Factors
In Bioinformatics 21:349-356, 2005.
[abstract] [pdf] supplementary data page VBLDS software

Beal, M.J.
Variational Algorithms for Approximate Bayesian Inference
PhD. Thesis, Gatsby Computational Neuroscience Unit, University College London, 2003. (281 pages)
[pdf] [ps.gz] or as individual chapters:
test Introduction

Ch 1: Introduction	(31)	[pdf 410k] [ps.gz 335k]
Ch 2: Variational Bayesian Theory	(38)	[pdf 491k] [ps.gz 379k]
Ch 3: Variational Bayesian Hidden Markov Models	(24)	[pdf 529k] [ps.gz 652k]
Ch 4: Variational Bayesian Mixture of Factor Analysers	(53)	[pdf 980k] [ps.gz 906k]
Ch 5: Variational Bayesian Linear Dynamical Systems	(47)	[pdf 1.1M] [ps.gz 1.3M]
Ch 6: Learning the structure of discrete-variable graphical models with hidden variables	(44)	[pdf 749k] [ps.gz 689k]

Nonparametric Bayesian / Infinite models

Teh, Y.W., Jordan, M.I., Beal, M.J. and Blei, D.M.
Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes
In Neural Information Processing Systems 17:1385-1392, MIT Press, 2005.
[ps] [pdf] [ps.gz] [pdf.gz] [djvu] [bibtex] NIPS 2004 project page
Beal, M.J., Ghahramani, Z. and Rasmussen, C.E.
The Infinite Hidden Markov Model
In Advances in Neural Information Processing Systems 14:577-584, eds. T. Dietterich, S. Becker, Z. Ghahramani, MIT Press, 2002.
[pdf] [ps.gz]

Variational Bayesian methods

Attias, H.T. and Beal, M.J.
Tree of Latent Mixtures for Bayesian Modelling and Classification of High Dimensional Data
Technical Report No. 2005-06. Dept. Computer Science and Engineering, University at Buffalo, SUNY. January 1, 2005.
[pdf] [ps.gz]
Beal, M.J. and Ghahramani, Z.
The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures
In Bayesian Statistics 7:453-464, Oxford University Press, 2003.
Preprint: [pdf] [ps.gz]. Volume preface [pdf] and contents [pdf]
Ghahramani, Z. and Beal, M.J.
Propagation Algorithms for Variational Bayesian Learning
In Advances in Neural Information Processing Systems 13:507-513, eds. T.K. Leen, T. Dietterich, V. Tresp, MIT Press, 2001.
[pdf] [ps.gz] [poster] [software]
Ghahramani, Z. and Beal, M.J.
Graphical Models and Variational Methods
Book chapter in Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket
Part of this work is that discussed at NIPS*99 workshop of the same title as the book.
[pdf] [ps.gz]
Ghahramani, Z. and Beal, M.J.
Variational Inference for Bayesian Mixtures of Factor Analysers
In Advances in Neural Information Processing Systems 12:449-455, eds. S. A. Solla, T.K. Leen, K. Müller, MIT Press, 2000.
[pdf] [ps.gz] [poster] [software]

Probabilistic Sensor Fusion

Beal, M.J., Jojic, N. and Attias, H.
A Graphical Model for Audio-Visual Object Tracking
In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), July 2003 (Vol. 25, No. 7), pp. 828--836.
[abstract] [pdf]
Beal, M.J., Attias, H. and Jojic, N.
Audio-Video Sensor Fusion with Probabilistic Graphical Models
In Proc. European Conf. on Computer Vision (ECCV), May 2002.
[LNCS abstract]
Beal, M.J., Jojic, N. and Attias, H.
A Self-Calibrating Algorithm for Speaker Tracking Based on Audio-Visual Statistical Models
In Proc. Int. Conf. on Acoustics Speech and Signal Proc. (ICASSP), May 2002.
[pdf] [ps.gz]

Other + Handwriting Recognition

Srinivasan, H., Srihari, S.N. and Beal, M.J.
Signature Verification using Kolmogorov-Smirnov Statistic
In Proc. 12th Conference of the International Graphonomics Society (IGS), Salerno, Italy, June 2005.
[pdf]
Srinivasan, H., Beal, M.J. and Srihari, S.N.
Machine Learning Approaches for Person Identification and Verification
In Conference on Homeland Security, Orlando, FL, March 2005, Society of Photo Instrumentation Engineers (SPIE).
[pdf]
Srihari, S.N., Beal, M.J., Bandi, K., Shah, V. and Krishnamurthy, P.
A Statistical Model for Writer Verification
In Proc. 8th International Conference on Document Analysis and Recognition (ICDAR), Seoul, Korea, October 2005.
[pdf]
Neal, R.M., Beal, M.J. and Roweis, S.T.
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models
In Neural Information Processing Systems 16:401-408, MIT Press, 2004.
[pdf] [ps.gz]

Miscellaneous

Classical and Bayesian approaches to reconstructing genetic regulatory networks (28th July 2004)
A poster presented at the 12th International Conference on Intelligent Systems for Molecular Biology (ISMB'04) in Edinburgh on 02/08/04.
[pdf] [ps.gz]
Variational Bayesian quick-reference sheet (updated 1st April 2002)
Something I cooked up during the summer of 2000. Ongoing, and not thoroughly checked for mistakes. This version is for A4 paper.
[pdf] [ps.gz] [html]
Variational Scoring of Graphical Model Structures (15/09/03)
A recent talk on VB structure learning that I gave at the Toronto Machine Learning group meetings.
[pdf]
Variational Inference in the Conjugate-Exponential Family (16/08/00)
A talk I gave to David J.C. MacKay's group in Inferential Sciences at Cambridge University. Many of the slides' contents are from the NIPS 2001 paper with Zoubin.
[pdf] [ps.gz]

Coming soon

Software for Hierarchical Dirichlet Processes is available from Yee Whye Teh's site.
Software is now available for input-dependent and simple VSSMs.
The Infinite HMM paper [pdf] is deeply related to work in collaboration with Yee Whye Teh, Mike Jordan, and David Blei --- see this page.

Quick links to collaborators

Hagai T. Attias	Golden Metallic
David M. Blei	CS, Princeton
Zoubin Ghahramani	Gatsby Computational Neuroscience Unit, London UK
Nebojsa Jojic	Microsoft Research, Seattle WA
Michael I. Jordan	Computer Science Division, UC Berkeley
Radford M. Neal	Statistics, Toronto ON
Carl E. Rasmussen	Max Planck Institute, Tübingen Germany
Sam T. Roweis	Computer Science, Toronto ON
Yee Whye Teh	Computer Science Division, UC Berkeley
David L. Wild	Keck Graduate Institute, Claremont CA