I401, Graduate School of Engineering Science, Osaka University, and JST CREST
kamatani at sigmath.es.osaka-u.ac.jp
1-3 Machikaneyama-cho, Toyonaka, Osaka, Japan
My background is Mathematical Sciences, and my main interest is in Bayesian Computation. Bayesian Statistics is a branch of Statistics (or Data Science), and Bayesian Computation helps evaluation of the posterior inference, which is the most important object for Bayesian Statistics. See monographs such as Doing Bayesian Data Analysis by Kruschke, and Bayesian Data Analysis by Gelman et al. See also The International Society for Bayesian Analysis webpage. You might be interested in a nice blog by Prof. A. Gelman and another excellent blog by C. P. Robert.
I am interested in the theoretical aspects of Bayesian Computation. More specificaly, I am working on the analysis of Markov chain Monte Carlo, Particle Filter/Sequential Monte Carlo and other related methods. In addition, I am interested in the inference of stochastic processes.
Paper: High-dimension MCMC for heavy-tail paper is now online at SPA. Also, a joint work with Japan Railway company is accepted at JSCE.
Sesstion Organize: Invited EcoSta at Taiwan, EAC-ISBA at Kobe, ISI at Malaysia and Contributed EMS at Palermo and BayesComp at Florida.
Slides: Reversible proposal MCMC with heavy-tailed target distributions at Singapore 2018