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Adaptive MCMC for infinite dimensional Bayesian inferences
Date:2015/10/30     Browse:465

Speaker: Jinglai Li

Time: Oct 30, 10:30am - 11:30am

Location: Room 310, Teaching Center, Zhangjiang Campus

Abstract:

Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, most standard Markov Chain Monte Carlo (MCMC) algorithms become arbitrary slow under the mesh refinement, which is referred to as being dimension dependent. On the other hand, in finite dimensional setting, adaptation is often used to improve the sampling efficiency of MCMC iterations. In this talk we present two adaptive MCMC algorithms for the infinite dimensional problems. We show that the proposed algorithms are dimension independent both theoretically and with numerical examples.

Bio:

Jinglai Li (李敬来)

Institute of Natural Sciences Shanghai Jiaotong University

 2012 : Distinguished Research Fellow, Shanghai Jiao Tong University

 2010 - 2012: Research Associate, MIT

 2007 - 2010: Research Associate, Northwestern University

 Ph.D.,2007,The State University of New York at Buffalo

Research Interests:

Scientific Computing, Computational Statistics, Uncertainty Quantification           

                                                                                SIST-Seminar 15046