MCMSKI IV
Plenary Speakers
Session 1: Andrew Gelman, Columbia University, New York
Session 2: Chris Holmes, University of Oxford
Invited Sessions
Approximate Bayesian Computation (org. Christian Robert)
Ajay Jasra, National University of Singapore, SG
Jean-Michel Marin, Université Montpellier II, France
Oliver Ratman, Imperial College London, UK and Duke University, USA
Session on scaling (or optimisation) of MCMC algorithms (org. Gareth Roberts)
Tony Lelievre, Ecole des Ponts ParisTech, France
Chris Sherlock, Lancaster University, UK
Jochen Voss, University of Leeds, UK
Alex Thiéry, University of Warwick, UK
Computational tools for Bayesian nonparametrics (org. Antonietta Mira and Antonio Lijoi)
Stefano Favaro, Università degli Studi di Torino, Italy
Yee Whye Teh, University of Oxford, UK
Ryan Adams, Harvard University, USA
Bayesian Microsimulation (org. Brad Carlin)
Laura Hatfield, Harvard Medical School, USA
Chris Jackson, MRC Biostatistics Unit, Cambridge, UK
Vanja Dukic, University of Colorado, USA
Convergence of MCMC and adaptive MCMC I (org. Gersende Fort and Jeff Rosenthal)
Yves Atchadé, University of Michigan, USA
Radu Craiu, University of Toronto, Canada
Galin Jones, University of Minnesota, USA
Convergence of MCMC and adaptive MCMC II (org. Gersende Fort and Jeff Rosenthal)
Gareth Roberts, University of Warwick, UK
Eric Moulines, Institut Télécom / Télécom ParisTech (ENST), France
Krys Łatuszyński, University of Warwick, UK
Advances in Sequential Monte Carlo methods (org. Christophe Andrieu)
Pierre Jacob, National University of Singapore, SG
Nick Whiteley, University of Britsol, UK
Adam Johansen, University of Warwick, UK
Anthony Lee, University of Warwick, UK
Convergence Rates of Markov Chains (org. Dawn Woodard)
Kshitij Khare, University of Florida, USA
Dawn Woodard, Cornell University, USA
Nayantara Bhatnagar, University of Delaware, USA
Discussant: Gersende Fort, LTCI, CNRS - TELECOM Paris Tech, France
Recent Developments in Software for MCMC - Round Table Session (org. Luke Bornn)
Andrew Thomas, University of Helsinki, Finland; Developer of BUGS
Martyn Plummer, International Agency for Research on Cancer, France; Developer of JAGS
Bob Carpenter, Columbia University, USA; Developer of STAN
Adrien Todeschini, INRIA Bordeaux, France; Developer of BiiPS
Contributed Sessions
Applications of MCMC (org. Radu Craiu)
Chiara Sabatti, Stanford University, USA
Samuel Kou, Harvard University, USA
Yuguo Chen, University of Illinois Urbana-Champaign, USA
Innovative Bayesian Computing in Astrophysics (org. David A. van Dyk)
Yaming Yu, University of California, Irvine, USA
Paul Baines, University of California, Davis, USA
Robertto Trotta, Imperial College London, UK
Alexandre Refregier, ETH Zurich, Switzerland
Differential geometry for Monte Carlo algorithms (org. Mark Girolami)
Sebastian Reich, University of Potsdam, Germany
Youssef Marzouk, Massachusetts Institute of Technology, USA
Simon Byrne, University College London, UK
Michael Betancourt, Massachusetts Institute of Technology, USA
Sampling and data assimilation for large models (org. Heikki Haario)
Kody Law, University of Warwick, UK
Tarek El Moselhy, Massachusetts Institute of Technology, USA
John Bardsley, University of Montana, USA
Sequential Monte Carlo for Static Learning (org. Robert B. Gramacy)
Chris Drovandi, Queensland University of Technology, AU
Christoforos Anagnostopoulos, Imperial College London, UK
Luke Bornn, Harvard University, USA
Matt Taddy, The University of Chicago, USA
Computational methods for Image analysis (org. Matthew Moore )
Lionel Cucala, Univeristy of Montpellier, FR
Mark Huber, Claremont McKenna College, USA
Matthew Moore,Queensland University of Technology, AU
Computational and Methodological Challenges in evidence synthesis and multi-step (modular models) (org. Nicky Best and Sylvia Richardson)
Martyn Plummer, Infections and Cancer Epidemiology Group, IARC, Lyon, FR
David Lunn, MRC Biostatistics Unit, UK
Christopher Paciorek, University of California, Berkeley, USA, and Perry de Valpine, University of California, Berkeley, USA
Pseudo-marginal and particle MCMC methods (org. M. Vihola)
Christophe Andrieu, University of Bristol, UK
Geoff Nicholls, University of Oxford, UK
Thomas Schön, Linkoping University, SE
Bayesian statistics and Population genetics (org. Mickael Blum and Olivier François)
Jukka Corander, Helsinki University, FI
Daniel Lawson, Bristol University, UK
Barbara Engelhardt, Duke University, USA
Bayesian Inference for Multivariate Dynamic Panel Data Models (org. Robert Kohn)
Sally Wood, Melbourne Business School, AU
Robert Kohn, Australian School of Business, AU
Francesco Bartolucci, Università di Perugia, IT
Monte Carlo methods in network analysis (org. Nial Friel)
David Hunter, Penn State University, USA
Adrian Raftery, University of Washington, USA
Ernst Wit (University of Groningen, NL
Advances in Monte Carlo motivated by applications (org. Robin Ryder)
Alexis Muir-Watt, University of Oxford, UK
Bayesian computation in Neurosciences (org. Nicolas Chopin and Simon Barthelmé)
Tim Johnson, University of Michigan, USA
Yan Zhou, University of Warwick, UK
Liam Paninski, Columbia University,USA
Probabilistic advances for Monte Carlo methods (org. Vivekananda Roy )
James Flegal, University of California, Riverside, USA
Radu Herbei, Ohio State University, USA
Sumeetpal Singh, University of Cambridge, UK
Vivekananda Roy Iowa State University, USA
Inference and Computation for High-dimensional Sparse Graphical Models (org. Guido Consonni)
Alex Lenkoski (Norwegian Computing Center, NO
Donatello Telesca, University of California at Los Angeles, USA
Hao Wang, University of South Carolina, USA
Discussant : Adrian Dobra, University of Washington, USA
Approximate Inference (org. Daniel Simpson)
Clare McGrory, University of Queensland, AU