## 統計数学セミナー

開催情報 火曜日　13:00～14:10　数理科学研究科棟(駒場) 052号室 http://www.sigmath.es.osaka-u.ac.jp/~kamatani/statseminar/ http://www.sigmath.es.osaka-u.ac.jp/~kamatani/statseminar/ 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う.

### 2016年10月31日(月)

15:40-16:30   数理科学研究科棟(駒場) 123号室
Kengo Kamatani 氏 (Osaka University, JST CREST)
Markov chain Monte Carlo for high-dimensional target distribution
[ 講演概要 ]
The Markov chain Monte Carlo (MCMC) algorithms are widely used to evaluate complicated integrals in Bayesian Statistics. Since the method is not free from the curse of dimensionality, it is important to quantify the effect of the dimensionality and establish an optimal MCMC strategy in high-dimension. In this talk, I will review some high-dimensional asymptotics of MCMC initiated by Roberts et. al. 97, and explain some asymptotic properties of the MpCN algorithm. I will also mention some connection to Stein-Malliavin techniques.