词条 | Pseudolikelihood |
释义 |
The pseudolikelihood approach was introduced by Julian Besag[1] in the context of analysing data having spatial dependence. DefinitionGiven a set of random variables and a set of dependencies between these random variables, where implies is conditionally independent of given 's neighbors, the pseudolikelihood of is Here is a vector of variables, is a vector of values. The expression above means that each variable in the vector has a corresponding value in the vector . The expression is the probability that the vector of variables has values equal to the vector . Because situations can often be described using state variables ranging over a set of possible values, the expression can therefore represent the probability of a certain state among all possible states allowed by the state variables. The pseudo-log-likelihood is a similar measure derived from the above expression. Thus One use of the pseudolikelihood measure is as an approximation for inference about a Markov or Bayesian network, as the pseudolikelihood of an assignment to may often be computed more efficiently than the likelihood, particularly when the latter may require marginalization over a large number of variables. PropertiesUse of the pseudolikelihood in place of the true likelihood function in a maximum likelihood analysis can lead to good estimates, but a straightforward application of the usual likelihood techniques to derive information about estimation uncertainty, or for significance testing, would in general be incorrect.[2] References1. ^{{citation |author=Besag, J. |year=1975 |title=Statistical Analysis of Non-Lattice Data |journal=The Statistician |volume=24 |issue=3 |pages=179–195 |jstor=2987782}} 2. ^Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, Oxford University Press. {{isbn|0-19-920613-9}} {{full citation needed|date=March 2017}} 1 : Statistical inference |
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