summary.bayes_mixture

Summary method for <code>bayes_mixture</code> objects
The summary of MCMC draws is given by the function
<code>summarise_draws</code> from package posterior.

A two-step Bayesian approach for mode inference following
Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>).
First, a mixture distribution is fitted on the data using a sparse finite
mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of
mixture components does not have to be known; the size of the mixture is
estimated endogenously through the SFM approach. Second, the modes of the
estimated mixture at each MCMC draw are retrieved using algorithms
specifically tailored for mode detection. These estimates are then used to
construct posterior probabilities for the number of modes, their locations
and uncertainties, providing a powerful tool for mode inference.

Paul Labonne

BayesMultiMode

Bayesian Mode Inference

Nalan Baştürk

Jamie Cross

Peter de Knijff

Lennart Hoogerheide

Herman van Dijk

summary.bayes_mixture function

<dl><dt>object</dt>
<dd>An object of class <code>bayes_mixture</code>.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Summary method for bayes_mixture objects
The summary of MCMC draws is given by the function
summarise_draws from package posterior. — summary.bayes_mixture

<dl>

<dt>object</dt>
<dd>An object of class <code>bayes_mixture</code>.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

Summary method for <code>bayes_mixture</code> objects
The summary of MCMC draws is given by the function
<code>summarise_draws</code> from package posterior.

summary.bayes_mixture: Summary method for `bayes_mixture` objects The summary of MCMC draws is given by the function `summarise_draws` from package posterior.

Description

Usage

Arguments