Linked Life Data

16422077

Source:http://linkedlifedata.com/resource/pubmed/id/16422077

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-1-20
pubmed:abstractText
Microwave imaging for dielectric objects was considered in this paper. Applying Bayesian approach to represent prior information about permittivity distribution of observed object by prior probability density and combine measurements information of scattering field, we obtained posterior probability density that included synthetic information about the observed object. And then, Gibbs sampler, one of Markov Chain Monte Carlo method, was used to sample the posterior probability density. The sample mean was regarded as an evaluation of the permittivity distribution. The results of simulation imaging with "blocky" objects showed that this set of methods made good use of information and had the advantages of feasibility and very strong anti-noise ability. In addition,it is capable of describing (definite or indefinite) prior information in a convenient and controllable way, as well as capable of giving the "complete" solution, i.e., the occurrence probability of every permittivity distribution.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1001-5515
pubmed:author
pubmed:issnType
Print
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1108-11
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
[Bayesian representation of prior information and MCMC method in microwave imaging].
pubmed:affiliation
College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China. zhaoxiang59@163.com
pubmed:publicationType
Journal Article, English Abstract, Research Support, Non-U.S. Gov't