Computing approximate monogenic model likelihoods in large pedigrees with loops

Approximate Likelihoods

In complex models likelihood functions may be difficult to compute, or depend on assumptions about high order dependencies that may be difficult to verify. A number of methods have been devised to compute inference functions either meant to approximate the true likelihood function, or to provide inferential summaries that balance statistical efficiency with ease of computation. Examples include...

Blocking Gibbs sampling for linkage analysis in large pedigrees with many loops.

We apply the method of "blocking Gibbs" sampling to a problem of great importance and complexity-linkage analysis. Blocking Gibbs sampling combines exact local computations with Gibbs sampling, in a way that complements the strengths of both. The method is able to handle problems with very high complexity, such as linkage analysis in large pedigrees with many loops, a task that no other known m...

Approximate Likelihoods for Spatial Processes

Maximum likelihood and related techniques are generally considered the best method for estimating the parameters of spatial models, but exact computation of the likelihood is slow when the number of data points is large. For Gaussian models with parametrically specified covariance function, we consider three alternatives to the exact maximum likelihood estimates that are easier to compute. Stat...

Consistent estimation using approximate likelihoods

PHRAPL: Phylogeographic Inference Using Approximate Likelihoods.

The demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori s...