kernel_walk

Generate a proposal for the Walk 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_walk 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>aw</dt>
<dd>Scale parameter `a_w` (default 1.5).</dd>
<dt>x</dt>
<dd>Current point `x`.</dd>
<dt>xp</dt>
<dd>Current point `x'`.</dd></dl>

Arguments

Generate a proposal for the Walk kernel — kernel_walk

<dl>

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


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


<dt>aw</dt>
<dd>Scale parameter `a_w` (default 1.5).</dd>


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


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

</dl>

kernel_walk: Generate a proposal for the Walk kernel

Description

Usage

Value

Arguments