Jump to content

Pseudolikelihood

From Wikipedia, the free encyclopedia

In statistical theory, a pseudolikelihood is an approximation to the joint probability distribution of a collection of random variables. The practical use of this is that it can provide an approximation to the likelihood function of a set of observed data which may either provide a computationally simpler problem for estimation, or may provide a way of obtaining explicit estimates of model parameters.

The pseudolikelihood approach was introduced by Julian Besag[1] in the context of analysing data having spatial dependence.

Definition

[edit]

Given a set of random variables the pseudolikelihood of is

in discrete case and

in continuous one. Here is a vector of variables, is a vector of values, is conditional density and is the vector of parameters we are to estimate. The expression above means that each variable in the vector has a corresponding value in the vector and means that the coordinate has been omitted. The expression is the probability that the vector of variables has values equal to the vector . This probability of course depends on the unknown parameter . 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, namely (in discrete case)

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.

Properties

[edit]

Use 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]

References

[edit]
  1. ^ Besag, J. (1975), "Statistical Analysis of Non-Lattice Data", The Statistician, 24 (3): 179–195, doi:10.2307/2987782, JSTOR 2987782
  2. ^ Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, Oxford University Press. ISBN 0-19-920613-9 [full citation needed]