SCBW01
30 October 2006 to 3 November 2006
Monte Carlo methods, particular Markov Chain Monte Carlo (MCMC), are now the methods of choice for making inferences about complex stochastic systems. Whilst MCMC dates back over 50 years, and there has been extensive research in Monte Carlo methods over the past 20 years, there are still many challenges that face researchers today.
These include attempting to analyse the highly complicated stochastic models and large scientific data sets that are now commonplace; and trying to understand the theoretical properties of some of the novel ideas that are proposed.
Currently, Monte Carlo methods are used by researchers in numerous scientific fields, including statistics, physics, engineering, genetics, econometrics, bioinformatics, and machine learning. This interdisciplinary workshop will bring together researchers from a variety of such fields to discuss current and novel Monte Carlo methodology, and to cross-fertilise ideas across these different disciplines. The workshop will have a broad focus, covering both recent advances in more established methods such as MCMC and sequential Monte Carlo, together with more recent ideas and ideas that have had little exposure within the statistics community, such as Variational methods, Population Monte Carlo, Approximate Bayesian Computing, Quasi Monte Carlo, and Indirect Inference.
Monday 30th October 2006 | |||
---|---|---|---|
08:30 to 10:00 | Room 1 | ||
10:00 to 10:05 |
Chris Wallace |
Room 1 | |
10:05 to 11:00 | Room 1 | ||
11:00 to 11:30 | No Room Required | ||
11:30 to 12:30 |
Tony Pettitt Queensland University of Technology |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
14:00 to 15:15 | Room 1 | ||
15:15 to 15:45 | No Room Required | ||
15:45 to 17:00 |
Arnoldo Frigessi University of Oslo |
Room 1 | |
17:00 to 18:00 | No Room Required | ||
18:15 to 18:45 | No Room Required |
Tuesday 31st October 2006 | |||
---|---|---|---|
09:00 to 10:00 |
Population-based MC for sampling trans-dimensional Bayesian regression models |
Room 1 | |
10:00 to 11:00 | Room 1 | ||
11:00 to 11:30 | No Room Required | ||
11:30 to 12:30 |
Olivier Cappe |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
14:00 to 15:15 | Room 1 | ||
15:15 to 15:45 | No Room Required | ||
15:45 to 17:00 | Discussion Room | ||
18:15 to 18:45 | No Room Required |
Wednesday 1st November 2006 | |||
---|---|---|---|
09:00 to 10:00 |
Yukito Iba The Institute of Statistical Mathematics (ISM) |
Room 1 | |
10:00 to 11:00 |
David Leslie University of Bristol |
Room 1 | |
11:00 to 11:30 | No Room Required | ||
11:30 to 12:30 |
Toby Johnson University of Leeds |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
14:00 to 15:15 | Room 1 | ||
15:15 to 15:45 | No Room Required | ||
15:45 to 17:00 |
The expected auxiliary variable method for Monte Carlo simulation |
Room 1 | |
18:15 to 18:45 | No Room Required |
Thursday 2nd November 2006 | |||
---|---|---|---|
09:00 to 10:00 | Room 1 | ||
10:00 to 11:00 | Room 1 | ||
11:00 to 11:30 | No Room Required | ||
11:30 to 12:30 |
Sequential Monte Carlo methods: can we replace the resampling step? |
Room 1 | |
12:30 to 13:30 | No Room Required | ||
14:00 to 15:15 | Room 1 | ||
15:15 to 15:45 | No Room Required | ||
15:45 to 17:00 |
Bayesian inference for nonlinear multivariate diffusion processes |
Room 1 | |
18:00 to 19:30 | No Room Required | ||
20:00 to 18:00 | No Room Required |
Friday 3rd November 2006 | |||
---|---|---|---|
09:00 to 10:00 |
Configurationally-Biased MC and Virtual-move parallel tempering |
Room 1 | |
10:00 to 11:00 |
Peter Green University of Bristol |
Room 1 | |
11:00 to 11:30 | No Room Required | ||
12:30 to 13:30 | No Room Required | ||
14:00 to 15:15 |
David Wales University of Cambridge |
Room 1 | |
15:15 to 15:45 | No Room Required | ||
15:45 to 17:00 |
Pierre L'Ecuyer Université de Montréal |
Room 1 | |
18:15 to 18:45 | No Room Required |
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