Scientific Organisers
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Mark Girolami, University of Warwick
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Antonietta Mira, Università della Svizzera Italiana & Università dell’Insubria
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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