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Durham Cathedral

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. BarberGraph decomposition for community identification and covariance constraints
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J. BesagSome statistical applications of constrained Monte Carlo
AND
Continuum limits of Gaussian Markov fields resolving the conflicts with geostatistics
pdf
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R.G. CowellA pot-pourri of Bayesian network learning methods pdf
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D.R. CoxDerived variables and graphical models
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J. CussensBayesian network learning by compiling to weighted MAX-SAT pdf
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A.P. DawidUsing influence diagrams for causal inference pdf
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V. DidelezG-Formula, Inverse Probability of Treatment Weighting and Optimal Sequential Treatments pdf
wmv

A. DobraBayesian structural learning and estimation in Gaussian graphical models and hierarchical log-linear models pdf
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M. DrtonGraphical methods for efficient likelihood inference in Gaussian covariance models pdf
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M. EichlerLearning causal structures in multivariate time series pdf
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J.J. ForsterBayesian (conditionally) conjugate inference for discrete data models pdf
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M. GoldsteinBayes linear graphical models and computer simulators for complex physical systems pdf
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S. HojsgaardRCOX models: Graphical Gaussian models with edge and vertex symmetries pdf
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S-H. KimMarkovian combination of decomposable model structures: MCMoSt pdf
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S.L. LauritzenIssues of existence of maximum likelihood estimators in Gaussian graphical models pdf
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G. LetacThe tiling of a junction tree by the minimal separators and its application to Wishart laws on non homogeneous decomposable graphs pdf
wmv

D. MaloucheGaussian covariance decomposition for PC-algorithm pdf
wmv

H. MassamFlexible Wisharts disctributions and their applications pdf
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F. MatusOn minimization of entropy functionals under moment constraints pdf
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J. MorteraSensitivity of inference in Bayesian networks to assumptions about founders pdf
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G. PistoneInformation geometry of graphical models pdf
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A. RoveratoThe non-rejection rate for structural learning of gene transcription networks from E.coli microarray data pdf
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S.C. ShawGeneralised Bayesian graphical modelling utilising Bayes linear kinematics pdf
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R. SilvaFactorial mixture of Gaussians and the marginal independence model pdf
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J.Q. SmithLarge sample robustness Bayes nets with incomplete information pdf
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M. StudenyOn Bayesian criteria for learning Bayesian network structure pdf
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S. SullivantAlgebraic aspects of Gaussian graphical models pdf
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Y.W. TehCollapsed variational inference for infinite state Bayesian networks
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P. VicardModel based and model assisted estimators using probabilistic expert systems pdf
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M. WainwrightMarginal polytopes of graphical models: Linear programs, max-product, and variational relaxation pdf
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N. WermuthProbability distributions with summary graph structure
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J. WhittakerBootstrapping divergence weighted independence graphs for design based survey analysis pdf
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D.A. WooffBayes linear revision for plates pdf
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H.P. WynnJunction tubes for Bayes nets and related algebra
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Posters
Introduction to the posters
F. BarthelPredicting survival using gene expression data and graphical models pdf


C. BlundellLearning Causal Bayesian Networks via Time Orderings


L. MalagòProbabilistic Graphical Models in Evolutionary Computation: from Estimation of Distribution Algorithms to the Combination of Population-Based and Model-Based Search


S. MassaCombining statistical models


F. MatusThe worst data for hierarchical log-linear models pdf

A. MazumderValue of Evidence Analysis Using Probabilistic Expert Systems - applications in planning for forensic DNA identification


H. NeufeldGraphical Gaussian Models with Symmetries pdf


K. PanayidouTree Learning and Variable Selection


R. RamsahaiSignificance Test for Instrumental Model


C. UhlerDetecting Interacting SNPs in Disease Association Studies Using MCMC on Multidimensional Contingency Tables


O. ZukThe sample complexity of learning Bayesian Networks ppt




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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