KAMATANI, Kengo/ 鎌谷 研吾
Associate Professor Graduate School of Engineering Science, Osaka University kamatani at sigmath.es.osaka-u.ac.jp 1-3 Machikaneyama-cho, Toyonaka, Osaka, Japan |

Information
- I will give a lecture series related to Markov chain and ergodicity at Graduate School of Mathematical Sciences at University of Tokyo (May 28 - Jun 1, 2018)
- Special Session Monte Carlo Methods will be held at The 12th AIMS Conference on Dynamical Systems, Differential Equations and Applications at Taipei (5 July - 9 July 2018). This session will focus more on the mathematical aspects of the Monte Carlo method compared to the Singapore workshop.
- A workshop Bayesian Computation for High-Dimensional Statistical Models will be held at Singapore (27 August - 21 September 2018). This will be an excellent opportunity to meet with Bayesian Monte Carlo researchers around the world.
- 2017.March.14: 日本評論社の現代統計学という本のうち，ベイズ統計に関する10ページほど担当させていただきました．
Research Interest
- Bayesian computation: Markov chain Monte Carlo, Sequential Monte Carlo and other related methods
- Asymptotic theory: Asymptotic theory for Bayesian inference, Asymptotic properties of Hidden Markov model
- Statistical Genetics: Statistical approach for genetics data with latent structure
Education
2003 B.A. Mathematics, Unviersity of Tokyo
2005 M.A. Mathematical Sciences, University of Tokyo 2008 Ph.D. Mathematical Sciences, University of Tokyo Professional Experience
07 Apr- 08 Sep, JSPS Research Fellowships for Young Scientists (DC2)
08 Oct- 09 Mar, JSPS Research Fellowships for Young Scientists (PD) 09 Apr-11 Oct, Project Research Associate at Graduate School of Mathematical Sciences, University of Tokyo 11 Oct-14 Mar, Assistant Professor at Graduate School of Engineering Science, Osaka University 14 Apr- Associate Professor at Graduate School of Engineering Science, Osaka University Professional Service
Associate Editor: Applied Probability Trust (2018-)
Publication
**Kengo Kamatani**. Efficient strategy for the markov chain monte carlo in high-dimension with heavy-tailed target probability distribution. Bernoulli, 24(4B):3711–3750, 2018. (doi:10.3150/17-BEJ976)- A. Jasra,
**K. Kamatani**, K. J. H. Law, and Y. Zhou. Bayesian static parameter estimation for partially observed diffusions via multilevel monte carlo. SIAM Journal on Scientific Computing, 40(2):A887–A902, 2018. (doi:10.1137/17M1112595) - Ajay Jasra,
**Kengo Kamatani**, Kody J. H. Law, and Yan Zhou. A multi-index markov chain monte carlo method. International Journal for Uncertainty Quantification, 8(1):61–73, 2018. - Ajay Jasra,
**Kengo Kamatani**, Prince Peprah Osei, and Yan Zhou. Multilevel particle filters: normalizing constant estimation. Statistics and Computing, 28(1):47–60, Jan 2018. (doi:10.1007/s11222-016-9715-5) **Kengo Kamatani**. Ergodicity of Markov chain Monte Carlo with reversible proposal. Journal of Applied Probability, 54:638–654, 2017.- Alexandros Beskos, Dan
Crisan, Ajay Jasra,
**Kengo Kamatani**, and Yan Zhou. A stable particle filter for a class of high-dimensional state-space models. Advances in Applied Probability, 49(1):24 UTF 201348, 2017. - A. Jasra,
**K. Kamatani**, K. J. H. Law, and Y. Zhou. Multilevel particle filters. SIAM Journal on Numerical Analysis, 55(6):3068–3096, 2017. (doi:10.1137/17M1111553) **Kengo Kamatani**, Akihiro Nogita, and Masayuki Uchida. Hybrid multi-step estimation of the volatility for stochastic regression models. Bulletin of Informatics and Cybernetics, 48:19–35, 2016.**Kengo Kamatani**and Masayuki Uchida. Hybrid multi-step estimators for stochastic differential equations based on sampled data. Statistical Inference for Stochastic Processes, 18(2):177–204, 2015. (doi:10.1007/s11203-014-9107-4)- Alexandre
Brouste, Masaaki Fukasawa, Hideitsu Hino,
Stefano M. Iacus,
**Kengo Kamatani**, Yuta Koike, Hiroki Masuda, Ryosuke Nomura, Teppei Ogihara, Yasutaka Shimuzu, Masayuki Uchida, and Nakahiro Yoshida. The yuima project: A computational framework for simulation and inference of stochastic differential equations. Journal of Statistical Software, 57(4):1–51, 2014. **Kengo Kamatani**. Asymptotic properties of Monte Carlo strategies for cumulative link model. Journal of the Japan Statistical Society, 44(1):1–23, 2014.**Kengo Kamatani**. Local consistency of Markov chain Monte Carlo methods. Ann. Inst. Statist. Math., 66(1):63–74, 2014. (doi:10.1007/s10463-013-0403-3)**Kengo Kamatani**. Local degeneracy of markov chain monte carlo methods. ESAIM: Probability and Statistics, 18:713–725, 1 2014. (doi:10.1051/ps/2014004)**Kengo Kamatani**. Local weak consistency of Markov chain Monte Carlo methods with application to mixture model. Bulletin of Informatics and Cybernetics, 45:103–123, 2013.**Kengo Kamatani**. Note on asymptotic properties of probit gibbs sampler. RIMS Kokyuroku, 1860:140–146, 2013.**Kengo Kamatani**. The order of degeneracy of markov chain monte carlo method. Journal of the Japan Statistical Society, 43(2):203–220, 2013.**Kengo Kamatani**. マルコフ連鎖モンテカルロ法のエルゴード性の解析. RIMS Kokyuroku, 1768:73–84, 2011.**Kengo Kamatani**. Metropolis-Hastings Algorithm for Mixture Model and its Weak Convergence. In Gilbert Lechevallier, Yves; Saporta, editor, Proceedings of COMPSTAT'2010, volume eBook, pages 1175–1182, 2010.**Kengo Kamatani**. Metropolis-Hastings algorithms with acceptance ratios of nearly 1. Ann. Inst. Statist. Math., 61(4):949–967, 2009. (doi:10.1007/s10463-008-0180-6)
Preprint
- A. N. Bishop, P. Del
Moral,
**K. Kamatani**, and B. Remillard. On one-dimensional Riccati diffusions. ArXiv e-prints, November 2017. - A. Jasra,
**K. Kamatani**, and H. Masuda. Bayesian inference for Stable Levy driven stochastic differential equations with high-frequency data, July 2017. **Kengo Kamatani**. Rate optimality of Random walk Metropolis algorithm in high-dimension with heavy-tailed target distribution. Arxiv, 2014.
Fellowships and Financial Aid
- Best Student Paper Award and Wakimoto Memorial Fund at the 5th IASC Asian Conf. on Stat. Comput., 2005
- JSPS Research Fellowships for Young Scientists (DC2), 2007 Apr -2009 Mar
- Grant-in-Aid for Scientific Research from the Ministry of Education, Japan, (Grant-in-Aid for Young Scientists (B) 22740055) 2010-2011
- Grant-in-Aid for Scientific Research from the Ministry of Education, Japan, (Grant-in-Aid for Young Scientists (B) 24740062) 2012--2015
- Grant-in-Aid for Scientific Research from the Ministry of Education, Japan, (Grant-in-Aid for Scientific Research (C) --) 2016--
- Ogawa Prize 2014, Japan Statistical Society
- Osaka Group, Mathematical statistics and stochastic analysis for modeling and analysis of complex random systems, JST CREST (Research Director: Nakahiro Yoshida, Univ. of Tokyo)
- Collaborative Research with Railway Technical Research Institute April 2017 - March 2018
Talks and forthcoming events
2018
- EcoSta 2018, Hong Kong, China
- Warwick, March
- ERCIM 2017, London, December
- JJSS, Nagoya, September
- (Course) Introduction to Markov chain Monte Carlo, Keio University, Kanagawa, July 31-Aug 4
- (Course) MCMC in high-dimension, Göttingen Data Science Summer School 2017, Göttingen, Germany, July 13
- Ergodicity of some reversible proposal MCMC and its application to Bayesian inference for stochastic processes, EcoSta 2017, Hong Kong, China, June 14-18
- MCMC in Yuima Package, ASC2017: Asymptotic Statistics and Computations, Komaba, Jan 30-Feb01
- MpCN法の幾何エルゴード性, 高精度情報抽出のための統計理論・方法論とその応用, Kyushu University, Nov 17-18
- Markov chain Monte Carlo for high-dimensional target distribution, Stochastic Analysis and Statistics 4, Komaba, Oct 31-Nov 01
- マルコフ連鎖モンテカルロ法の高次元解析,JJS, Kanazawa, Sep 5-7
- Some properties of the mixed preconditioned Crank-Nicolson algorithm, IMS-APRM, Hong Kong, China, June, 27-30
- Reversible proposal MCMC in high dimension, SIAM Conference on Uncertainty Quantification (UQ16), Lausanne, Switzerland, Apr, 5-8
- High-dimensional asymptotic properties of Markov chain Monte Carlo methods for heavy-tailed target distributions, Paris, Mar 23
- マルコフ連鎖のエルゴード性とregular variation, 日本統計学会春季集会, Tohoku, Mar 5
- Ookayama, Tokyo, Feb
- Ergodicity of MpCN and related MCMC algorithms, Asymptotic Statistics and Computations, Komaba, Tokyo, Feb 15
- Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distributions, MCMski V, Lenzerheide, Switzerland, Jan, 5-7
- Efficient strategy for the Markov chain Monte Carlo in high-dimension and its implementation, IASC-ARS, Singapore, Dec
- On some ergodic properties of the MpCN algorithm, ERCIM, London, Dec
- マルコフ連鎖モンテカルロ法の高次元漸近論, 大規模複雑データの理論と方法論:最前線の動向, Tsukuba, Nov
- 対称な提案を有するMCMCの高次元解析, Tokyo, Oct
- 高次元でのマルコフ連鎖モンテカルロ法, CREST Meeting, Tokyo, Sep
- Asymptotic theory of Markov chain Monte Carlo method in high-dimension, MSJ, Kyoto, Sep
- 高次元での高速なマルコフ連鎖モンテカルロ法, JSS, Okayama, Sep
- Efficient strategy of MCMC in high-dimension and its application to diffusion processes, SAPS-X, LeMans, France, Mar [ PDF ]
- Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution, NUS, Singapore, Feb
- Efficient construction of MCMC in high-dimension, 大規模統計モデリングと計算統計, Komaba, Feb
- Hybrid multi-step estimators for stochastic differential equations based on sampled data, ERCIM, Pisa, Italy, Dec
- Rate optimality of Random walk Metropolis algorithm in high-dimension with heavy-tailed target distribution, Niigata
- マルコフ連鎖モンテカルロ法の退化性の評価, Tokei-kanren gakkai, Tokyo, Sep
- Rate optimality of Random walk Metropolis algorithm in high-dimension with heavy-tailed target distribution, DynStoch 2014, Warwick, UK, Sep
- The order of degeneracy of Markov chain Monte Carlo Method, IMS-APRM 2014, Taipei, Chinese Taipei, 1st, Jul
- Rate optimality of Markov chain Monte Carlo in high-dimension, Osaka, Apr
- Weak consistency for Metropolis-Hastings algorithm in high-dimension, Komaba, Mar
- マルコフ連鎖モンテカルロ法の拡散過程への収束, Kunitachi, Feb
- (Poster) Local consistency of Markov chain Monte Carlo with some applications, MCMSki 2014, Chamonix, France, Jan
- マルコフ連鎖モンテカルロ法の一致性, Kyusyu, Nov
- 混合モデルのマルコフ連鎖モンテカルロ法の漸近理論, Tokei-kanren gakkai,Osaka, Sep
- 混合モデルのベイズ推定, Summer seminar, Hiroshima, Aug
- Various order of degeneracies of Markov chain Monte Carlo for categorical data, EMS2013, Budapest, 20-25th, July
- Order of degeneracy of several MCMCs, Asymptotic Statistics and Computations 2013, Komaba, 27-28th, Mar
- Non degenerate MCMC for categorical model, Asymptotic Expansions for Various Models and Their Related Topics, RIMS, Kyoto, 4-6th, Mar
- Contiguous proposal for Metropolis-Hastings algorithms, SART2012, Komaba, Tokyo, 19-22th Dec
- Non-regular model for Bayesian computation, Takanawa, Tokyo, 18-19th Dec
- (Poster) Efficient Monte Carlo Strategy for Simple Mixture Model, SuSTaIn, Bristol, UK, 25th, Sep
- Local consistency of MCMC and its application to cumulative link model, BigMC, Institut Henri Poincare, Paris, France, 20th, Sep
- Markov chain Monte Carlo methods for simple mixture model, IMS-APRM 2012, Tsukuba, July
- Efficiency of Monte Carlo methods, Waseda University, Tokyo, 30th, June
- (Poster) Asymptotic properties of Monte Carlo strategies for cumulative link model, ISBA 2012, Kyoto, 27th, June [ Abst ]
- Weak consistency of Markov chain Monte Carlo, Seminar on Probability, Osaka, 8th, May [ Abst ]
- ベイズ統計学におけるモンテカルロ法, Open Seminar of Data Science, Osaka, 25th Apr
- Asymptotic properties of MCMC for cumulative link model, Seminar on Probability and Statistics, Komaba, Apr
- Local degeneracy of MCMC for cumulative logit model, SART2011, Komaba, Dec
- マルコフ連鎖モンテカルロ法の退化性 (Degeneracy of Markov chain Monte Carlo methods), Statistics Summer Seminar, Suwa, Nagano, June
- MCMCの収束の決定論的扱いと退化性 (Decision theoretic view of MCMC and its degeneracy), 数理統計学の新たな展開, Tsukuba, June
- ベイズ統計学でのData Augmentationの手法 (Data augmentation strategy in Bayesian statistics), Emergent Dynamics in Nonlinear Science, Komaba, May
- Weak Convergence of Markov chain Monte Carlo II, Asymptotic Statistics of Stochastic Processes - VIII, Le Mans, France, Mar [ PDF ]
- Weak Convergence of Markov chain Monte Carlo, Statistical inference and numerical analysis of stochastic processes, Florence, Italy, Mar [ PDF ]
- Convergence of MCMC for Simple Binomial Model, Statistics for Stochastic Processes, Komaba, Tokyo, Feb
- MCMC法の解析における漸近的方法, RIMS, Kyoto, Nov
- Convergence of Markov measure valued random variables and its application to MCMC, SPA 2010, Senri life science center, Osaka, Sep
- マルコフチェインモンテカルロ法のエルゴード性の解析, マクロ経済動学の非線形数理, RIMS, Kyoto, Sep
- マルコフチェインモンテカルロ法の弱収束, Toukei Kanren Gakkai Rengo Taikai, Waseda University, Tokyo, Sep
- Metropolis-Hastings Algorithm for Mixture Model and its Weak Convergence, COMPSTAT 2010, CNAM, Paris, Aug [ PDF ]
- Validity of the EM algorithm for haplotype frequency estimation, Summer Seminar, Izu, Shizuoka, Aug
- Weak convergence of Markov chain Monte Carlo method and its application to Yuima, Seminar on Probability and Statistics, UT, Komaba, Tokyo, Jun
- Weak convergence of the Gibbs sampler and its application to mixture model, Stochastic Analysis of the Advanced Statistical Models, Hiroshima University, Hiroshima, Mar
- Weak convergence of the Gibbs sampler for some simple models, Stochastic Analysis and Statistical Inference V, UT, Komaba, Tokyo, Feb
- Weak Convergence of the Gibbs sampler, Seminar on Statistics, UT, Hongo, Tokyo, Jan
- Non-regular Behaviors of Monte Carlo Methods for finite Mixture Models, Theory and Applications on Statistical Inference and Probability Analysis and Related Areas, Akita city, Akita, Dec
- Irregular Behaviors of the Gibbs Sampler for the Mixture Model with Strong Identifiability Conditions, Recent Results on Mathematical Statistics and Related Areas, Takasaki city, Gunma, Nov
- Asymptotic behavior of the Gibbs sampler, Statistics Young Summer Seminar, Fukui city, Fukui, Aug
- Some Non-regular Models for the EM Algorithm and the Gibbs Sampler, Stochastic Analysis and Statistical Inference VI, UT, Komaba, Tokyo, Feb
- On some asymptotic properties of the Gibbs sampler, CASTA2008, Kyoto University, Kyoto, Dec
- Convergence properties of the Gibbs sampler and related algorithms, Stochastic Analysis and Statistical Inference III, Komaba, Nov
- On some asymptotic properties of the EM algorithm, Stat. Sem., UT, Hongo, Oct
- Large sample theory for the EM algorithm and the Gibbs sampling, Efficient Monte Carlo, Sonderborg, Denmark, July [ PDF ]
- Asymptotic behaviors of the Gibbs sampling, Stochastic Analysis and Statistical Inference II, Komaba, Feb
- Asymptotic Statistics for Haplotype Association Study, Problems on Statistical Decision Theory, Hokkaido, Dec
- Local properties for Markov chain Mote Carlo algorithm, Stochastic Analysis and Statistical Inference, Komaba, Nov
- Haplotype Association Study: Approaches from MCMC, Stat. Sem., UT, Hongo, Nov
- Haplotype Association Study: Approaches from EM algorithm, Sem. on Prob. & Stat., UT, Komaba, Nov
- A Note on Haplotype estimation, Math. Sci. on Stat. Model, ISM, Azabu, Nov
- A Note on Haplotype estimation, Sem. on Prob. & Stat., UT, Komaba, Nov
- A Note on Haplotype estimation, Stat. Sem., UT, Hongo, Nov
- Central Limit Theorem for polynomial ergodic Markov chain, COE poster session, UT, Komaba, Sep
- A Note on Haplotype Estimation, Stat. Summer Sem., Saitama, Aug
- MHs with high acceptance ratios, the 5th IASC Asian Conf. on Stat. Comput., Univ. of Hong Kong, China, Dec
- MHs with acceptance ratios of nearly 1, Asymp. Methods for Prob. and Stat., Komaba, Dec
- MHs with acceptance ratios of nearly 1, COE poster session, UT, Komaba, Sep
- MHs with acceptance ratios of nearly 1, Stat. Summer Sem., Ohita, Aug
- MH whose acceptance rate is almost 1, Stat. Sem., UT, Hongo, Jun
- MH whose acceptance rate is almost 1, Sem. on Prob. & Stat., UT, Komaba, Jun
- Transformed MH with polynomial or geometrical ergodicity, Sem. on Prob. & Stat., UT, Komaba, Dec
- Adapted MH using histogram estimate, Stat. Summer Sem., Wakayama, Aug
- Adapted MH using histogram estimate, Stat. Sem., UT, Hongo, Jun
- Markov chain in a general state space, Sem. on Prob. & Stat., UT, Komaba, Jan
MH: Metropolis-Hastings Algorithm, UT: University of Tokyo, COE: Centers of Excellence, ISM: Institute of Statistical Mathematics Lecture University of Tokyo10,11 Summer, 確率モデルと統計手法演習 2018 May 28 - June 2, Ergodicity of Markov chain Osaka University11 Winter 数学IB演習 12-14, Summer, 計算数理A 11-18 Winter, Statistics C2 14 Winter, 15-18 Summer Time series analyis, 14 Winter Mathematics C Göttingen2017 July 13, MCMC in high-dimension, Göttingen Data Science Summer School 2017 Keio University2017 July 31 to August 4. Introduction to Markov chain Monte Carlo. Teaching Assistant 04 Summer, Teaching Asistant, Computing Mathematics for the third year students 05 Summer, Teaching Assitant, Mathematics I(A) for the first year students 06 Summer, Teaching Assitant, Mathematics I(A) for the first year students 06 Winter, Teaching Assitant, Probability and Statistics for the third year students Research Assistant 05 Apr- 06 Mar, Research Assistant, supported by COE 06 Apr- 07 Mar, Research Assistant, supported by COE Other activities - 05 Apr- now, one of the organizers of Seminar on Probability and Statistics
- Yuima Project Developer. Yuima is a project for simulation and inference of multidimensional stochastic differential equations in R.
- 確率の応用例，東京大学高校生のための現代数学講座 (lectures for highschool students in Gunma pref.), Tambara seminar house, Gunma, Jul
- One of the organizers of Statistics Young Summer Seminar 2012
- 君は現代の予言者になれるか, 2016年度 東京大学高校生のための現代数学講座, テーマ「確率と統計」, 玉原
Current students
Mixutani, Naoki (水谷 尚樹) and Song Xiaolin (宋 小林)
Past students
2015 April - 2017 March, Goto, Fumitaka (後藤 史威). Master's thesis "An extension and practical performances of MpCN algorithm"
2014 April - 2016 March, Ogawa, Masahiro (小川 真弘). Master's thesis "Hawkes model の数値的解析"
Statistical Inference Research Group | Division of Mathematical Science for Social Systems | Osaka University
Statistics Group | Graduate School of Mathematical Sciences | University of Tokyo Seminar on Probability and Statistics | Probability Seminar (Grad. Scl. of Science) | CSFI Seminar Yuima Project (R-Forge) | R Project CREST Project (Prof. Yoshida) | Multidisciplinary Research Laboratory System (Prof. Kiyono) |

KAMATANI, Kengo WEB |