Linked Life Data

8409623

Source:http://linkedlifedata.com/resource/pubmed/id/8409623

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1993-10-22
pubmed:abstractText
Markov chain Monte Carlo (MCMC) methods have been explored by various researchers as an alternative to exact probability computation in statistical genetics. The objective is to simulate a Markov chain with the desired equilibrium distribution. If the transition kernel is aperiodic and irreducible, then convergence to the equilibrium distribution is guaranteed; realizations of the Markov chain can thus be used to estimate desired probabilities. Aperiodicity is easily satisfied, but, although it has been shown that irreducibility is satisfied for a diallelic locus, reducibility is a potential problem for a multiallelic locus. This is a particularly serious problem in linkage analysis, because multiallelic markers are much more informative than diallelic markers and thus highly preferred. In this paper, the authors propose a new algorithm to achieve irreducibility of the Markov chain of interest by introducing an irreducible auxiliary chain. The irreducibility of the auxiliary chain is obtained by assigning positive probabilities to a small subset of the genotypic configurations inconsistent with the data, to bridge the gap between the irreducible sets.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0265-0746
pubmed:author
pubmed:issnType
Print
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1-17
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
1993
pubmed:articleTitle
Achieving irreducibility of the Markov chain Monte Carlo method applied to pedigree data.
pubmed:affiliation
Department of Statistics, University of Washington, Seattle 98195.
pubmed:publicationType
Journal Article