Asymptotic analysis of statistical computation methods for hidden Markov models

2015 Fiscal Year Final Research Report

• Project/Area Number: 24740062
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: General mathematics (including Probability theory/Statistical mathematics)
• Research Institution: Osaka University
• Principal Investigator: Kamatani Kengo 大阪大学, 基礎工学研究科, 講師 (00569767)
• Project Period (FY): 2012-04-01 – 2016-03-31
• Keywords: ベイズ統計 / モンテカルロ / 漸近理論 / 高次元解析 / 複雑モデル / 大規模データ

Outline of Final Research Achievements

For the project, I performed analysis on (a) sequential monte carlo methods and (b) markov chain monte carlo (MCMC) methods for high-dimensional complicated models. For (a), we proposed (a-1) efficient strategy for high-dimensional state space models, and (a-2) ensemble strategy with multi-level monte carlo. For (b) we proposed a scale-free MCMC and analysed its performance via high-dimensional asymptotic and ergodicity analysis.

Free Research Field

統計学