fit_dirichlet

C++ implementation of the fixed-point iteration algorithm by Minka (2000).

Estimates the precision of transdimensional Markov chain Monte Carlo
(MCMC) output, which is often used for Bayesian analysis of models with different
dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible
jump MCMC) relies on sampling a discrete model-indicator variable to estimate
the posterior model probabilities. If only few switches occur between the models,
precision may be low and assessment based on the assumption of independent
samples misleading. Based on the observed transition matrix of the indicator
variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019,
Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws
posterior samples of the stationary distribution to (a) assess the uncertainty
in the estimated posterior model probabilities and (b) estimate the effective
sample size of the MCMC output.

Daniel W. Heck

MCMCprecision

Precision of Discrete Parameters in Transdimensional MCMC

fit_dirichlet function

<dl><dt>x</dt>
<dd>a matrix of Dirichlet samples, one row per observation.</dd>
<dt>const</dt>
<dd>constant that is added to avoid problems with zeros in <code>log(x)</code>.
The default is <code>const = min(x[x&gt;0])*.01</code>.</dd>
<dt>maxit</dt>
<dd>maximum number of iterations.</dd>
<dt>abstol</dt>
<dd>The absolute convergence tolerance: maximum of absolute differences of Dirichlet parameters.</dd></dl>

Arguments

Estimate Parameters of Dirichlet Distribution — fit_dirichlet

<dl>

<dt>x</dt>
<dd>a matrix of Dirichlet samples, one row per observation.</dd>


<dt>const</dt>
<dd>constant that is added to avoid problems with zeros in <code>log(x)</code>.
The default is <code>const = min(x[x&gt;0])*.01</code>.</dd>


<dt>maxit</dt>
<dd>maximum number of iterations.</dd>


<dt>abstol</dt>
<dd>The absolute convergence tolerance: maximum of absolute differences of Dirichlet parameters.</dd>

</dl>

fit_dirichlet: Estimate Parameters of Dirichlet Distribution

Description

Usage

Arguments

Details

References

See Also

Examples