Welcome

I am Kengo Kamatani Professor at ISM, 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 the 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 specifically, 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. Here are some recent papers: 

• Boomerang sampler (joint work with J. Bierkens, S. Grazzi and G. O. Roberts)

•  Non-reversible guided Metropolis-Hastings kernel. (joint work with X. Song)

• An efficient strategy for the Markov chain Monte Carlo in high-dimension with a heavy-tailed target probability distribution.

I am a member of the JST CREST project launched in October 2021, "New developments in statistics for stochastic systems toward data science for large-scale spatiotemporal dependence" (Research Director: Prof. Nakahiro Yoshida, The University of Tokyo), and this work is ongoing. The earlier JST CREST area “Modeling Methods allied with Modern Mathematics” has concluded; within that program we advanced statistical inference for complex dependent systems and developed user‑friendly tools such as the YUIMA Project.

Collaboration with the Railway Technical Research Institute is also continuing.