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Kerrie Mengersen, Queensland University of Technology
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Christian Robert, Université Paris-Dauphine & University of Warwick
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Mike Titterington, University of Glasgow
About:
Statistical mixture distributions are used to model scenarios in which certain variables are measured but a categorical variable is missing. For example, although clinical data on a patient may be available their disease category may not be, and this adds significant degrees of complication to the statistical analysis. The above situation characterises the simplest mixture-type scenario; variations include, among others, hidden Markov models, in which the missing variable follows a Markov chain model, and latent structure models, in which the missing variable or variables represent model-enriching devices rather than real physical entities.
Speakers
Murray Aitkin, University of Melbourne - How Many Normal Components in the Galaxy Velocity Data? Posterior Deviance Distributions for the Number of Components, and their Interpretation
Clare Alston, Queensland University of Technology - Bayesian Mixture Models: a Blood Free Dissection of a Sheep
Christophe Andrieu, University of Bristol - Exact Approximations of MCMC Algorithms
Olivier Cappé, Telecom ParisTech & CNRS - Online EM Algorithms for Mixtures, HMMs and Beyond
Jiahua Chen, University of British Columbia - Testing the Order of Finite Mixture Models by EM-Test
Kim-Anh Do, University of Texas - Bayesian Mixture Modelling with Applications to Translational Cancer Research
Paul Fearnhead, Lancaster University - Sequential Monte Carlo Methods and Perfect Sampling for Mixture Models
Sylvia Fruehwirth-Schnatter, Johannes Kepler University Linz - Dealing with Label Switching Under Model Uncertainty
Richard Gerlach, University of Sydney - Smooth Transition Mixture GARCH Models for Forecasting Risk Measures in Financial Markets
John Geweke, University of Technology - Interpretation and Inference in Mixture Models
Mark Girolami, University of Glasgow - Inferring Spectral Mixture Components in Multiplexed Surface Enhanced Raman Resonance Spectroscopy
Katherine Heller, University of Cambridge - The IBP Compound Dirichlet Process and its Application to Focused Topic Modelling
Chris Holmes, University of Oxford - Investigations in Variable Selection for Bayesian Mixture Models
Michael Jordan, University of California - Applied Bayesian Nonparametrics
Michael Jordan, University of California - Completely Random Measures, Hierarchy and Nesting in Bayesian Nonparametrics
Robert Kohn, University of New South Wales - Bayesian Mixtures of Autoregressive Models
Bruce Lindsay, Pennsylvania State University - Mixture Related Analysis in Many Dimensions
Geoff McLachlan, University of Queensland - The Modelling of High-Dimensional Data via Normal Mixture Models
Kerrie Mengersen, Queensland University of Technology - Where Are They and What Do They Look Like? Discovering Patterns in Data Using Statistical Mixture Models
Peter Müller, University of Texas - Bayesian Semiparametric Mixture Models with Covariate-Dependent Weights
Iain Murray, University of Toronto & University of Edinburgh - Sampling Latent Variable Models
Brendan Murphy, University College Dublin - A Mixture of Experts Latent Position Cluster Model for Social Network Data
Michael Newton, University of Wisconsin - Gamma-Based Clustering via Ordered Means with Application to Gene-Expression Analysis
Yee Whye Teh, University College London - On Hierarchical Clustering, Partitions and Mixture Models
Chris Williams, University of Edinburgh - Greedy Learning of Binary Latent Trees