学术报告

Convergence of Spectral Likelihood Approximation Based on Q-Hermite Polynomials for Bayesian Inverse Problems

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题目:Convergence of Spectral Likelihood Approximation Based on Q-Hermite Polynomials for Bayesian Inverse Problems
报告人:邓志亮 教授 (电子科技大学)
时间:2018年12月25日10:00-11:00
地点:致远楼103室
摘要:  In this paper, q-Gaussian distribution, q-analogy of Gaussian distribution, is introduced to characterize the prior information of unkown parameters for inverse problems. Based on Q-Hermite polynominals, we propase a spectral likelihood approximation algorithm of Bayesian inversion. Convergence results of the approximated posterior distribution in the sense of KullbackpLeibler divergence are obtained when the likelihood function is replaced with the SlA and the prior density function is truncated to its partial sum. In the end, two numerical examples are displayed, which verify our results.

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