Workshop Summary
- Name of workshop
- Bayesian Inference and Stochastic Computation 2012 workshop
- Host organization
- The Institute of Statistical Mathematics
- Website
- http://daweb.ism.ac.jp/~yoshidar/BayesComp/
- Date
- June 22-23, 2012
- Location
- The Institute of Statistical Mathematics (ISM), Tachikawa, Tokyo Japan
Access to ISM - Registration
- Close Registration
- Related events
- ISBA 2012 World Meeting, Kyoto Japan (June 25-29)
Website of ISBA 2012 - Kanto Branch of Ecological Society of Japan Symposium:
"Hierarchical modelling for the environmental sciences" (Special lectures by Prof. Alan Gelfand and Prof. James S Clark at University of Tokyo, June 21)
Symposium website - NAOJ Seminar 2012 (a talk by Prof. Phil Gregory at National Astronomical Observatory of Japan, June 25)
Aim of Workshop
Recent progress in Bayesian modeling enables us to treat the nonstationary, inhomogeneous, and non-Gaussian nature of real-world data. Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), and other stochastic computational methods play essential roles in this innovative data analysis.
The main aim of this conference is to discuss the recent developments in these fields.
However, we want to go beyond this paradigm; our central query is to extend the concept of "stochastic computation based on stochastic modeling".
MCMC and SMC are generic tools for generating high-dimensional random variates and estimating their probabilities; hence, we can apply these methods to other problems of sampling rare events, counting discrete structures, testing, and so on. Thus, it is natural to explore novel applications of MCMC and SMC.
Another area of interest is to unify Bayesian computation to large-scale, multilevel simulation on massively parallel hardware. This approach is sometimes referred to as "data assimilation by stochastic approach".
In this conference, we will collectively explore the key areas of the future of stochastic computation; we hope that it reactivates the Bayesian belief that everything is probabilistic.
List of Speakers (more details)
- Mike West (Duke University, USA)
Bayesian analysis, Dynamic models, Stochastic computation - Alan Gelfand (Duke University, USA)
Space and space-time data analysis for environmental processes - Genshiro Kitagawa (ROIS, Japan)
Statistical modeling, State-space model - James S Clark (Duke University, USA)
Ecology and population biology, Models for ecological data - Rob Kass (Carnegie Mellon University, USA)
Bayesian inference, Neuroscience, Neural spike train analysis - Phil Gregory (University of British Columbia, Canada)
Bayesian inference in astronomy - Jorge Kurchan (École Supérieure de Physique et de Chimie Industrielles, France)
Statistical physics - John Geweke (University of Technology Sydney, Australia)
Econometrics, Monte Carlo methods for Bayesian inference - Arnaud Doucet (Oxford University, UK)
Monte Carlo methods, Bayesian statistics - Ryo Yoshida (ISM, Japan)
Statistical science, Systems biology - Yukito Iba (ISM, Japan)
Statistical science, Statistical physics, Monte Carlo algorithms
