compute_normalization_constant

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

compute_normalization_constant function

<dl><dt>counts</dt>
<dd>integer vector of counts</dd>
<dt>n_start</dt>
<dd>start of prior support range</dd>
<dt>n_end</dt>
<dd>end of prior support range</dd>
<dt>f_product</dt>
<dd>product of (1-<code>fractions</code>)</dd></dl>

Arguments

Compute normalization constant — compute_normalization_constant

<dl>

<dt>counts</dt>
<dd>integer vector of counts</dd>


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


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


<dt>f_product</dt>
<dd>product of (1-<code>fractions</code>)</dd>

</dl>

compute_normalization_constant: Compute normalization constant

Description

Usage

Value

Arguments