A Bayesian Markov chain Monte Carlo solution of the bilinear problem

被引：0

作者：

Ochs, AF ^{[1
]}

Stoyanova, RS ^{[1
]}

Brown, TR ^{[1
]}

Rooney, WD ^{[1
]}

Springer, CS ^{[1
]}

机构：

[1] Fox Chase Canc Ctr, Philadelphia, PA 19111 USA

来源：

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING | 2001年 / 567卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many problems in imaging reduce to a desire to identify physically significant components within a set of images gathered during the variation of a parameter. We present a new method to identify physically meaningful regions in a series of images through the application of Bayesian statistics within a Markov chain Monte Carlo sampler. The method Ends the physically meaningful bilinear solution appropriate to the problem.

引用

页码：274 / 284

页数：11

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