PosteriorFrame

Calculate the posterior mean and quantiles from a Dirichlet process object.

Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the 'dirichletprocess' package to handle the
Markov chain Monte Carlo sampling.
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
See Teh, Y. W. (2011)
<https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>,
among many other sources.

Dean Markwick

dirichletprocess

Build Dirichlet Process Objects for Bayesian Modelling

Gordon J. Ross

Kees Mulder

Giovanni Sighinolfi

Filippo Fiocchi 

PosteriorFrame function

<dl><dt>dpobj</dt>
<dd>The Dirichlet process object to be drawn from.</dd>
<dt>xgrid</dt>
<dd>The x values the posterior is to be evaluated at.</dd>
<dt>ndraws</dt>
<dd>The number of posterior draws to take.</dd>
<dt>ci_size</dt>
<dd>The size of the credible interval draw in terms of percentage.</dd></dl>

Arguments

Calculate the posterior mean and quantiles from a Dirichlet process object. — PosteriorFrame

<dl>

<dt>dpobj</dt>
<dd>The Dirichlet process object to be drawn from.</dd>


<dt>xgrid</dt>
<dd>The x values the posterior is to be evaluated at.</dd>


<dt>ndraws</dt>
<dd>The number of posterior draws to take.</dd>


<dt>ci_size</dt>
<dd>The size of the credible interval draw in terms of percentage.</dd>

</dl>

PosteriorFrame: Calculate the posterior mean and quantiles from a Dirichlet process object.

Description

Usage

Value

Arguments