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London Mathematical Society Durham Symposium Mathematical Aspects of Graphical Models Monday 30th June - Thursday 10th July 2008 A PDF reader is available free from adobe while WMV files can be viewed using Windows Media Player or RealPlayer whose basic versions are free. On-line talks Speaker Title PDF WMV Bonus D. Barber Graph decomposition for community identification and covariance constraints J. Besag Some statistical applications of constrained Monte Carlo AND Continuum limits of Gaussian Markov fields resolving the conflicts with geostatistics R.G. Cowell A pot-pourri of Bayesian network learning methods D.R. Cox Derived variables and graphical models J. Cussens Bayesian network learning by compiling to weighted MAX-SAT A.P. Dawid Using influence diagrams for causal inference V. Didelez G-Formula, Inverse Probability of Treatment Weighting and Optimal Sequential Treatments A. Dobra Bayesian structural learning and estimation in Gaussian graphical models and hierarchical log-linear models M. Drton Graphical methods for efficient likelihood inference in Gaussian covariance models M. Eichler Learning causal structures in multivariate time series J.J. Forster Bayesian (conditionally) conjugate inference for discrete data models M. Goldstein Bayes linear graphical models and computer simulators for complex physical systems S. Hojsgaard RCOX models: Graphical Gaussian models with edge and vertex symmetries S-H. Kim Markovian combination of decomposable model structures: MCMoSt S.L. Lauritzen Issues of existence of maximum likelihood estimators in Gaussian graphical models G. Letac The tiling of a junction tree by the minimal separators and its application to Wishart laws on non homogeneous decomposable graphs D. Malouche Gaussian covariance decomposition for PC-algorithm H. Massam Flexible Wisharts disctributions and their applications F. Matus On minimization of entropy functionals under moment constraints J. Mortera Sensitivity of inference in Bayesian networks to assumptions about founders G. Pistone Information geometry of graphical models A. Roverato The non-rejection rate for structural learning of gene transcription networks from E.coli microarray data S.C. Shaw Generalised Bayesian graphical modelling utilising Bayes linear kinematics R. Silva Factorial mixture of Gaussians and the marginal independence model J.Q. Smith Large sample robustness Bayes nets with incomplete information M. Studeny On Bayesian criteria for learning Bayesian network structure S. Sullivant Algebraic aspects of Gaussian graphical models Y.W. Teh Collapsed variational inference for infinite state Bayesian networks P. Vicard Model based and model assisted estimators using probabilistic expert systems M. Wainwright Marginal polytopes of graphical models: Linear programs, max-product, and variational relaxation N. Wermuth Probability distributions with summary graph structure J. Whittaker Bootstrapping divergence weighted independence graphs for design based survey analysis D.A. Wooff Bayes linear revision for plates H.P. Wynn Junction tubes for Bayes nets and related algebra Posters Introduction to the posters F. Barthel Predicting survival using gene expression data and graphical models C. Blundell Learning Causal Bayesian Networks via Time Orderings L. Malagò Probabilistic Graphical Models in Evolutionary Computation: from Estimation of Distribution Algorithms to the Combination of Population-Based and Model-Based Search S. Massa Combining statistical models F. Matus The worst data for hierarchical log-linear models A. Mazumder Value of Evidence Analysis Using Probabilistic Expert Systems - applications in planning for forensic DNA identification H. Neufeld Graphical Gaussian Models with Symmetries K. Panayidou Tree Learning and Variable Selection R. Ramsahai Significance Test for Instrumental Model C. Uhler Detecting Interacting SNPs in Disease Association Studies Using MCMC on Multidimensional Contingency Tables O. Zuk The sample complexity of learning Bayesian Networks Special thanks to Graeme Hickey and David Randell for shooting the movies. The owners of the website, and the lecturers giving the talks, have no responsibility for, and will accept no liability arising from, the content of the videos. The videos may be used for educational purposes only, and use in any commercial context is forbidden.

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