approach to modelling Bayesian non-parametrics and the probabilistic

[1]  Sonia Petrone,et al.  Hierarchical reinforced urn processes , 2012 .

[2]  Zoubin Ghahramani,et al.  An Infinite Latent Attribute Model for Network Data , 2012, ICML.

[3]  Roman Garnett,et al.  Bayesian Quadrature for Ratios , 2012, AISTATS.

[4]  Yee Whye Teh,et al.  Modelling Genetic Variations using Fragmentation-Coagulation Processes , 2011, NIPS.

[5]  Phil Blunsom,et al.  A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction , 2011, ACL.

[6]  Zoubin Ghahramani,et al.  Pitman-Yor Diffusion Trees , 2011, UAI.

[7]  Thomas L. Griffiths,et al.  The Indian Buffet Process: An Introduction and Review , 2011, J. Mach. Learn. Res..

[8]  Lars Kai Hansen,et al.  Infinite multiple membership relational modeling for complex networks , 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

[9]  Andrew Gordon Wilson,et al.  Generalised Wishart Processes , 2010, UAI.

[10]  Zoubin Ghahramani,et al.  Bayesian Time Series Models: Nonparametric hidden Markov models , 2011 .

[11]  Radford M. Neal.  Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .

[12]  Zoubin Ghahramani,et al.  Scaling the iHMM: Parallelization versus Hadoop , 2010, 2010 10th IEEE International Conference on Computer and Information Technology.

[13]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[14]  Ryan P. Adams,et al.  Learning the Structure of Deep Sparse Graphical Models , 2009, AISTATS.

[15]  Peter Orbanz,et al.  Construction of Nonparametric Bayesian Models from Parametric Bayes Equations , 2009, NIPS.

[16]  Y. Teh,et al.  Indian Buffet Processes with Power-law Behavior , 2009, NIPS.

[17]  Thomas L. Griffiths,et al.  Nonparametric Latent Feature Models for Link Prediction , 2009, NIPS.

[18]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[19]  C. Gouriéroux,et al.  The Wishart Autoregressive Process of Multivariate Stochastic Volatility , 2009 .

[20]  Tai Sing Lee,et al.  The Block Diagonal Infinite Hidden Markov Model , 2009, AISTATS.

[21]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[22]  Yee Whye Teh,et al.  The Infinite Factorial Hidden Markov Model , 2008, NIPS.

[23]  Joshua B. Tenenbaum,et al.  Church: a language for generative models , 2008, UAI.

[24]  Yee Whye Teh,et al.  Beam sampling for the infinite hidden Markov model , 2008, ICML '08.

[25]  Michael I. Jordan,et al.  An HDP-HMM for systems with state persistence , 2008, ICML '08.

[26]  W. Richards,et al.  Perception as Bayesian Inference , 2008 .

[27]  P. McCullagh,et al.  Gibbs fragmentation trees , 2007, 0704.0945.

[28]  Yee Whye Teh,et al.  Bayesian Agglomerative Clustering with Coalescents , 2007, NIPS.

[29]  Zoubin Ghahramani,et al.  Infinite Sparse Factor Analysis and Infinite Independent Components Analysis , 2007, ICA.

[30]  Michael I. Jordan,et al.  Hierarchical Beta Processes and the Indian Buffet Process , 2007, AISTATS.

[31]  Zoubin Ghahramani,et al.  Modeling Dyadic Data with Binary Latent Factors , 2006, NIPS.

[32]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[33]  J. Tenenbaum,et al.  Optimal Predictions in Everyday Cognition , 2006, Psychological science.

[34]  M. McAleer,et al.  Multivariate Stochastic Volatility: A Review , 2006 .

[35]  M. Glickman,et al.  Multivariate Stochastic Volatility via Wishart Processes , 2006 .

[36]  Thomas L. Griffiths,et al.  Infinite latent feature models and the Indian buffet process , 2005, NIPS.

[37]  Charles M. Bishop,et al.  Variational Message Passing , 2005, J. Mach. Learn. Res..

[38]  O. Kallenberg.  Probabilistic Symmetries and Invariance Principles , 2005 .

[39]  Zoubin Ghahramani,et al.  A note on the evidence and Bayesian Occam's razor , 2005 .

[40]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[41]  Nir Friedman,et al.  Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.

[42]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[43]  Carl E. Rasmussen,et al.  Bayesian Monte Carlo , 2002, NIPS.

[44]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[45]  Carl E. Rasmussen,et al.  Factorial Hidden Markov Models , 1997 .

[46]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[47]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[48]  Radford M. Neal.  Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .

[49]  Carl E. Rasmussen,et al.  Occam's Razor , 2000, NIPS.

[50]  Carl E. Rasmussen,et al.  The Infinite Gaussian Mixture Model , 1999, NIPS.

[51]  Zoubin Ghahramani,et al.  Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.

[52]  William D. Penny,et al.  Bayesian Approaches to Gaussian Mixture Modeling , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Radford M. Neal.  Assessing Relevance determination methods using DELVE , 1998 .

[54]  J. Pitman,et al.  The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator , 1997 .

[55]  David Bruce Wilson,et al.  Exact sampling with coupled Markov chains and applications to statistical mechanics , 1996, Random Struct. Algorithms.

[56]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[57]  P Donnelly,et al.  Coalescents and genealogical structure under neutrality. , 1995, Annual review of genetics.

[58]  Zoubin Ghahramani,et al.  Factorial Learning and the EM Algorithm , 1994, NIPS.

[59]  Eric Saund,et al.  Unsupervised Learning of Mixtures of Multiple Causes in Binary Data , 1993, NIPS.

[60]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[61]  D. Mackay,et al.  Bayesian methods for adaptive models , 1992 .

[62]  James O. Berger,et al.  Ockham's Razor and Bayesian Analysis , 1992 .

[63]  A. O'Hagan,et al.  Bayes–Hermite quadrature , 1991 .

[64]  S H Chung,et al.  Characterization of single channel currents using digital signal processing techniques based on Hidden Markov Models. , 1990, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[65]  N. Hjort.  Nonparametric Bayes Estimators Based on Beta Processes in Models for Life History Data , 1990 .

[66]  A. Zellner.  Optimal Information Processing and Bayes's Theorem , 1988 .

[67]  P. Diaconis.  Bayesian Numerical Analysis , 1988 .

[68]  L. L. Cam,et al.  Asymptotic Methods In Statistical Decision Theory , 1986 .

[69]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[70]  D. Aldous.  Exchangeability and related topics , 1985 .

[71]  Albert Y. Lo,et al.  On a Class of Bayesian Nonparametric Estimates: I. Density Estimates , 1984 .

[72]  T. Ferguson.  BAYESIAN DENSITY ESTIMATION BY MIXTURES OF NORMAL DISTRIBUTIONS , 1983 .

[73]  R. Muirhead.  Aspects of Multivariate Statistical Theory , 1982, Wiley Series in Probability and Statistics.

[74]  C. Antoniak.  Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .

[75]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[76]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .