model_likelihood: Computes the likelihood of a model from samples from its...
In mailund/admixture_graph: Admixture Graph Manipulation and Fitting
Description
Usage
Arguments
Value
Description
The likelihood of a graph can be computed by integrating over all the graph parameters (with appropriate priors).
Doing this by sampling from priors is very inefficient, so we use samples from the posteriors to importance
sample the likelihood.
Usage
1
model_likelihood(log_likelihoods)
Arguments
log_likelihoods
Samples of log likelihoods from the posterior distribution of the graph.
Value
The likelihood of a graph where graph parameters are integrated out.
mailund/admixture_graph documentation built on May 21, 2019, 11:06 a.m.
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Description Usage Arguments Value
Description
The likelihood of a graph can be computed by integrating over all the graph parameters (with appropriate priors). Doing this by sampling from priors is very inefficient, so we use samples from the posteriors to importance sample the likelihood.
Usage
1 | model_likelihood(log_likelihoods)
|
Arguments
log_likelihoods |
Samples of log likelihoods from the posterior distribution of the graph. |
Value
The likelihood of a graph where graph parameters are integrated out.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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