Advances in Markov Chain Monte Carlo

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Advances in Markov Chain Monte Carlo

 23 - 25 Apr 2012

ICMS

Scientific Organisers

  • Mark Girolami, University of Warwick
  • Antonietta Mira, Università della Svizzera Italiana & Università dell’Insubria
  • Christian Robert, Université Paris-Dauphine & University of Warwick

About:

The popularisation and entry into mainstream statistical practice of Markov chain Monte Carlo (MCMC) methods and associated simulation algorithms over the last twenty years has been due to its use of Bayesian statistical inference. MCMC methods for statistical inference are routinely being deployed in the basic sciences such as genetics, physics and biology. The recent methodological advances in MCMC presented an opportunity to gather leading experts within the UK and Europe.

Speakers and their talk titles

Christophe Andrieu, University of Bristol - Stability and Stabilisation of Controlled Markov Chains and their Applications in Statistics  

Yves Atchade, University of Michigan - Computing Bayes Factors with Confidence

Alex Beskos, Univeristy College London - Hamiltonian Dynamics in High Dimensions

Nicolas Chopin, ENSAE-CREST - My Memory is Long, My Patience is Not: How Not to Use MCMC for Bayesian Spectral Density Estimation for Long-Memory Processes

Maria de Iorio, University College London - A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures

Arnaud Doucet, University of Oxford - Derivative Free Estimates of the Score Vector and Observed Information Matrix 

Gersende Fort, LTCI, CNRS - Stochastic Approximation for Adaptive Interacting MCMC Samplers

Nial Friel, Univeristy College Dublin - Estimating the Evidence for Doubly Intractable Distributions

Andrew Golightly, Newcastle University - Exact Inference for Stochastic Kinetic Models via a Linear Noise Approximation

Jim Griffin, University of Kent - Adaptive Monte Carlo Methods for Variable Selection

Heiki Haario, Lappeenranta University of Technology - State and Parameter Estimation for Large Scale Models

Jim Hobert, University of Florida - Convergence Rate Results for Two Gibbs Samplers

Daniele Imparato, Università dell'Insubria - Density Estimators through Zero Variance Markov Chain Monte Carlo

Marko Laine, Finnish Meteorological Institute - Efficient Adaptive MCMC for Complex Models

Krysztof Latuszynski, University of Warwick - Robustness of Manifold MALA and Related Algorithms

Dan Lawson, University of Bristol - The Dirichlet Process in Genetics

Faming Liang, Texas A&M University - Bayesian Subset Modeling for High Dimensional Generalised Linear Models and its Asymptotic Properties

Jean-Michel Marin, Université Montpellier 2 - Bayesian Inference on a Mixture Model with Spatial Dependence

Xiao Li Meng, Harvard University - Interweaving Residual Augmentations

Iain Murray, University of Edinburgh - Sampling Hierarchical Latent Gaussian Models

Omiros Papaspiliopoulos, Universitat Pompeu Fabra - Path Augmentation

Natesh Pillai, Harvard University - Recent Advances in High Dimensional Covariance Matrix Estimation

Gareth Roberts, University of Warwick - Why Does the Gibbs Sampler Work on Hierarchical Models?

Simo Sarkka, Aalto University - Posterior Inference on Parameters of Stochastic Differential Equations via Gaussian Process Approximations

Andrew Stuart, University of Warwick - Random Walk Metropolis Algorithms in High Dimensions

Gareth Tribello, ETH - Methods for Surveying Complex Probability Distributions

David van Dyke, Imperial College London - Metropolis Hastings Within Partially Collapsed Gibbs Samplers, with Application in High-Energy Astrophysics

Darren Wilkinson, Newcastle University - Bayesian Inference for Markov Processes with Application to Biochemical Network Dynamics

Nick Whiteley, University of Bristol - Stability Properties of Particle Filters