dirch_from_counts

Takes a vector of counts for 1:n collections,
and returns a Dirichlet random variable generated by adding the prior to each
collection value, and simulating an alpha from a gamma distribution
with this shape parameter.

internal

Implements Bayesian inference for the conditional genetic
stock identification model. It allows inference of mixed fisheries and also
simulation of mixtures to predict accuracy. A full description of the underlying
methods is available in a recently published article in the
Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.

Eric C. Anderson

rubias

Bayesian Inference from the Conditional Genetic Stock
Identification Model

dirch_from_counts function

<dl><dt>C</dt>
<dd>a vector giving counts of categories</dd>
<dt>lambda</dt>
<dd>priors for the categories</dd></dl>

Arguments

Given a vector of counts for different categories in 1...n and a prior,
simulate a Dirichlet random vector — dirch_from_counts

<dl>

<dt>C</dt>
<dd>a vector giving counts of categories</dd>


<dt>lambda</dt>
<dd>priors for the categories</dd>

</dl>

dirch_from_counts: Given a vector of counts for different categories in 1...n and a prior, simulate a Dirichlet random vector

Description

Usage

Arguments

Details