Issue No 11


Workshopon Statistical mixtures and latent-structure modelling
28–30 March

Scientific Organising Committee:
Christian Robert (Paris)
Michael Titterington (Glasgow)

Supported by:
The Engineering and Physical Sciences Research Council
The Royal Statistical Society

The motivation for the Workshop was the awareness that the topic of mixture modelling and the wider versions that constitute latent structure analysis are currently of major interest to an increasingly wide range of scientific disciplines. A number of aspects underpinned the eventual format of the Workshop and the choice of invitees:
  1. the fact that mixture models and their generalisations are not amenable to standard theory in some likelihood-ratio testing contexts, with work ongoing to deal with this non-regularity;
  2. the ubiquity of the EM algorithm for obtaining maximum likelihood or maximum a posteriori estimates of parameters in incomplete-data contexts;
  3. the recent exploitation of Markov chain Monte Carlo simulation methods for facilitating practical inference in both Bayesian and likelihood approaches to latent-structure models;
  4. the surge of interest during the last few years among the neural computing community in models involving latent structure, especially in the context of very large scale practical problems
The formal programme began with an overview lecture by Professor Robert and was wound up by a ‘summing-up’ session led by Professor Titterington. In between there were 18 invited lectures and there was time for a poster session at which 14 items were on display. In advance of the poster session the presenters were allowed two-minute spotlight opportunities at which they could give very brief trailers of the posters and thereby attract interest to the displayed posters. All presentations were based on research that was either very recent or still under development.

The speakers (of whom 11 were based overseas) and titles for the invited talks were the following.
M Aitkin: A general mixture ML analysis for random effect models
C Andrieu: SAME, SA2ME, FAME and RDA
C M Bishop: Variational methods and latent variables
G Celeux: Assessing the number of mixture components: a survey
P Dellaportas: Latent variables for modelling volatility processes
B Garel: Likelihood ratio test for univariate Gaussian mixture
E Gassiat: The number of populations in a mixture with Markov regime
P J Green: Mixtures in time and space
B G Lindsay: On determining an adequate number of mixture components
N L Hjort: On attempts at generalising the Dirichlet process
D J C MacKay: The state of the art in error correcting codes
G J McLachlan: On the incremental EM algorithm for speeding up the fitting of finite mixture models
R M Neal: Hierarchical mixtures using diffusion tree priors
G O Roberts: Bayesian inference for discretely observed diffusion processes
T Rydén: Continuous-time jump MCMC and model selection for HMMs
C Skinner: Estimation of distributions in the presence of measurement error
M Stephens: Inferring latent population structure from genetic data
C K I Williams: Image modelling with dynamic trees
Attendance at the Workshop had been strictly limited, partly because of the constraints imposed by the venue at the ICMS, but also by the desire to create an intimate ambience. Seven places were allocated to PhD students who were selected, from those who responded to a call for expressions of interest, on the basis of the perceived relevance of the Workshop to their research project.

The dominant aspects of the Workshop were the following
  1. Even the simple mixture models continue to attract much attention, in terms of classical frequentist theory (Garel), in the development of modifications to the now-standard EM algorithm (McLachlan), in the study of distance-based estimators (Besbeas) and in computational and other aspects of the Bayesian approach (Andrieu, Nobile, Mengersen).
  2. In both simple mixtures and more complicated latent structure models, there is still considerable activity concerning the thorny problem of assessing the cardinality of the latent state space, e.g. ‘the number of components in a mixture’ (Gassiat, Lindsay, Celeux, Stephens). The approaches varied and included the search for valid frequentist asymptotic theory, the use of model-selection criteria and Bayesian analysis of variable-dimension parameter spaces.
  3. The general nature of latent structure modelling was much in evidence, as in the variations corresponding to random effects models and measurement error models (Aitkin, Skinner, Richardson), situations involving spatio-temporal effects (Green), diffusion process (Roberts), Dirichlet processes (Hjort, Rasmussen), hierarchical structures (Neal), hidden Markov models (Gassiat, Rydén), graphical models (Salgueiro) and mixtures of factor analysers (Seeger).
  4. The pervasiveness of the models in application areas was remarkable, including genetics (Stephens), finance (Dellaportas), solar radiation (Glasbey), social statistics (Salgueiro), epidemiology (Mooney), error-correcting codes (MacKay), forensic science (Aitken), ornithology (Darnell) and many others referred to in passing throughout the Workshop. (v) Although the majority of the participants were from the statistical
  5. community, a sizeable and prominent minority were at least partially based in computer science environments, even though their research interests matched those of the Workshop very well. This led to a very positive atmosphere of mutual enlightenment and interest. Although there was some agreement to differ in some areas, the overall impression was given that everyone gleaned useful knowledge; for instance, for many statisticians this was their first exposure to so-called variational approximations (Bishop, Beal).
Informal feedback from the participants has been universally and gratifyingly positive, principally concerning the quality of the scientific material and its presentation, but significantly concerning the pleasant, relaxed atmosphere provided by the ICMS premises in James Clerk Maxwell’s place of birth.

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Aitken, Colin – University of Edinburgh
Aitkin, Murray – Education Statistics Services Centre
Andrieu, Christophe – University of Bristol
Beal, Matt – University College London
Besbeas, Panagiotis – University of Kent
Bishop, Christopher – Microsoft Research
Celeux, Gilles – INRIA
Chauveau, Didier – Université Marne-la-Vallee
Croft, Jonathan – University of Warwick
Darnell, Ross – University of Newcastle-upon-Tyne
Dellaportas, Petros – Athens Univ. of Economics & Business
Ecob, Russell – Ecob Consulting
Fokoué, Ernest – University of Glasgow
Garel, Bernard – National Polytechnic Institute, Toulouse
Gassiat, Elisabeth – Université Paris-Sud
Glasbey, Chris – Biomathematics & Statistics Scotland
Green, Peter – University of Bristol
Hjort, Nils – University of Oslo
Hurn, Merrilee – University of Bristol
Kauermann, Göran – University of Glasgow
Lindsay, Bruce – Pennsylvania State University
MacKay, David – University of Cambridge
McLachan, Geoff – University of Queensland
Mengersen, Kerrie – University of Newcastle, Australia
Mooney, Jenny – University of Aberdeen
Murphy, Brendan – Trinity College Dublin
Murray-Smith, Roderick – University of Glasgow
Neal, Radford – University of Toronto
Nobile, Agostino – University of Glasgow
Rasmussen, Carl – University College, London
Richardson, Sylvia – Imperial School of Medicine
Robert, Christian – Université Paris IX Dauphine
Roberts, Gareth – University of Lancaster
Rydén, Tobias – University of Lund
Salgueiro, Maria – University of Southampton
Seeger, Matthias – University of Edinburgh
Shi, Jian Qing – University of Glasgow
Skinner, Chris – University of Southampton
Stephens, Matthew – University of Washington
Storkey, Amos – University of Edinburgh
Titterington, Mike – University of Glasgow
Wild, Pascal – INRS
Williams, Chris – University of Edinburgh
Worton, Bruce – University of Edinburgh

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