gamma_poisson_clough

Compute posterior probability using a Gamma-Poisson model (Clough et al.)

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) <doi:10.1371/journal.pone.0074388>.

Federico Comoglio

dupiR

Bayesian Inference from Count Data using Discrete Uniform Priors

Maurizio Rinaldi

gamma_poisson_clough function

<dl><dt>object</dt>
<dd>object of class <code>Counts</code></dd>
<dt>n_start</dt>
<dd>start of prior support range</dd>
<dt>n_end</dt>
<dd>end of prior support range</dd>
<dt>a</dt>
<dd>prior shape parameter of the gamma distribution used to compute the posterior with Clough. Default to 1</dd>
<dt>b</dt>
<dd>prior rate parameter of the gamma distribution used to compute the posterior with Clough. Default to 1e-10</dd></dl>

Arguments

Compute posterior probability using a Gamma-Poisson model (Clough et al.) — gamma_poisson_clough

<dl>

<dt>object</dt>
<dd>object of class <code>Counts</code></dd>


<dt>n_start</dt>
<dd>start of prior support range</dd>


<dt>n_end</dt>
<dd>end of prior support range</dd>


<dt>a</dt>
<dd>prior shape parameter of the gamma distribution used to compute the posterior with Clough. Default to 1</dd>


<dt>b</dt>
<dd>prior rate parameter of the gamma distribution used to compute the posterior with Clough. Default to 1e-10</dd>

</dl>

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gamma_poisson_clough: Compute posterior probability using a Gamma-Poisson model (Clough et al.)

Description

Usage

Value

Arguments