logpoissgamma

Computes the logarithm of the posterior density of a Poisson log mean
with a gamma prior

models

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Jim Albert

LearnBayes

Functions for Learning Bayesian Inference

logpoissgamma function

<dl> <dt>theta</dt>
<dd>vector of values of the log mean parameter</dd> <dt>datapar</dt>
<dd>list with components data, vector of observations, and par, vector of
parameters of the gamma prior</dd></dl>

Arguments

Author

Log posterior with Poisson sampling and gamma prior — logpoissgamma

<dl>

 <dt>theta</dt>
<dd>vector of values of the log mean parameter</dd>

 <dt>datapar</dt>
<dd>list with components data, vector of observations, and par, vector of
parameters of the gamma prior</dd>

</dl>

logpoissgamma: Log posterior with Poisson sampling and gamma prior

Description

Usage

Value

Arguments

Author

Examples