kernel_traverse

Generate a proposal for the Traverse kernel

internal

Implements the t-walk algorithm, a general-purpose, self-adjusting
Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as
described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires
no tuning and is robust for a wide range of target distributions, including
high-dimensional and multimodal problems. This implementation includes an
option for running multiple chains in parallel to accelerate sampling and
facilitate convergence diagnostics.

Rodrigo Fonseca Villa

Rtwalk

An MCMC Sampler Using the t-Walk Algorithm

kernel_traverse function

<dl><dt>n_dim</dt>
<dd>Dimension of the parameter space.</dd>
<dt>p_phi</dt>
<dd>Probability of updating each coordinate.</dd>
<dt>x</dt>
<dd>Current point `x`.</dd>
<dt>xp</dt>
<dd>Current point `x'`.</dd>
<dt>beta</dt>
<dd>Step parameter beta.</dd></dl>

Arguments

Generate a proposal for the Traverse kernel — kernel_traverse

<dl>

<dt>n_dim</dt>
<dd>Dimension of the parameter space.</dd>


<dt>p_phi</dt>
<dd>Probability of updating each coordinate.</dd>


<dt>x</dt>
<dd>Current point `x`.</dd>


<dt>xp</dt>
<dd>Current point `x'`.</dd>


<dt>beta</dt>
<dd>Step parameter beta.</dd>

</dl>

kernel_traverse: Generate a proposal for the Traverse kernel

Description

Usage

Value

Arguments