samples.gmrfdpgrow

provides posterior sampled values for every model parameter of a
<code>gmrfdpgrow</code> object

Estimates a collection of time-indexed functions under
either of Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations where a Dirichlet process
mixture allows sub-groupings of the functions to share the same
covariance or precision parameters.  The GP and iGMRF formulations
both support any number of additive covariance or precision terms,
respectively, expressing either or both of multiple trend and
seasonality.

Terrance Savitsky

growfunctions

Bayesian Non-Parametric Dependent Models for Time-Indexed
Functional Data

samples.gmrfdpgrow function

<dl><dt>object</dt>
<dd>A <code>gmrfdpgrow</code> object</dd>
<dt>...</dt>
<dd>Ignored</dd></dl>

Arguments

Produce samples of MCMC output — samples.gmrfdpgrow

<dl>

<dt>object</dt>
<dd>A <code>gmrfdpgrow</code> object</dd>


<dt>...</dt>
<dd>Ignored</dd>

</dl>

samples.gmrfdpgrow: Produce samples of MCMC output

Description

Usage

Arguments