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Tokyo Probability Seminar
Seminar information archive ～05/23｜Next seminar｜Future seminars 05/24～
| Date, time & place | Monday 16:00 - 17:30 126Room #126 (Graduate School of Math. Sci. Bldg.) |
|---|---|
| Organizer(s) | Makiko Sasada, Shuta Nakajima, Masato Hoshino |
Seminar information archive
2015/05/18
16:50-18:20 Room #128 (Graduate School of Math. Sci. Bldg.)
Lu Xu (Graduate School of Mathematical Sciences, The University of Tokyo)
Central limit theorem for stochastic heat equations in random environments
Lu Xu (Graduate School of Mathematical Sciences, The University of Tokyo)
Central limit theorem for stochastic heat equations in random environments
2015/05/11
16:50-18:20 Room #128 (Graduate School of Math. Sci. Bldg.)
Naoyuki Ichihara (College of Science and Engineering, Aoyama Gakuin University)
Phase transitions for controlled Markov chains on infinite graphs (JAPANESE)
Naoyuki Ichihara (College of Science and Engineering, Aoyama Gakuin University)
Phase transitions for controlled Markov chains on infinite graphs (JAPANESE)
2015/04/20
16:50-18:20 Room #128 (Graduate School of Math. Sci. Bldg.)
Tetsuya Hattori (Faculty of Economics, Keio University)
TBA (JAPANESE)
Tetsuya Hattori (Faculty of Economics, Keio University)
TBA (JAPANESE)
2015/04/13
16:50-17:50 Room #128 (Graduate School of Math. Sci. Bldg.)
Hans Rudolf Kuensch (ETH Zurich)
Modern Monte Carlo methods -- Some examples and open questions (ENGLISH)
Hans Rudolf Kuensch (ETH Zurich)
Modern Monte Carlo methods -- Some examples and open questions (ENGLISH)
[ Abstract ]
Probability and statistics once had strong relations, but in recent years the two fields have moved into opposite directions. Despite this, I believe that both fields would profit if they continued to interact. Monte Carlo methods are one topic that is of interest to both probability and statistics: Statisticians use advanced Monte Carlo methods, and analyzing these methods is a challenge for probabilists. I will illustrate this, using as examples rare event estimation by sample splitting, approximate Bayesian computation and Monte Carlo filters.
Probability and statistics once had strong relations, but in recent years the two fields have moved into opposite directions. Despite this, I believe that both fields would profit if they continued to interact. Monte Carlo methods are one topic that is of interest to both probability and statistics: Statisticians use advanced Monte Carlo methods, and analyzing these methods is a challenge for probabilists. I will illustrate this, using as examples rare event estimation by sample splitting, approximate Bayesian computation and Monte Carlo filters.
