期刊
INVERSE PROBLEMS
卷 35, 期 8, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6420/ab15a3
关键词
Poisson distribution; Laplace prior; expectation propagation; approximate Bayesian inference
资金
- EPSRC [EP/M025160/1, EP/M020533/1] Funding Source: UKRI
The Poisson distribution arises naturally when dealing with data involving counts, and it has found many applications in inverse problems and imaging. In this work, we develop an approximate Bayesian inference technique based on expectation propagation for approximating the posterior distribution formed from the Poisson likelihood function and a Laplace type prior distribution, e.g. the anisotropic total variation prior. The approach iteratively yields a Gaussian approximation, and at each iteration, it updates the Gaussian approximation to one factor of the posterior distribution by moment matching. We derive explicit update formulas in terms of one-dimensional integrals, and also discuss stable and efficient quadrature rules for evaluating these integrals. The method is showcased on two-dimensional PET images.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据