util_prepPcheckpoints

Used in 'util_Pcheckpoints()' within 'learn()'.

internal

Calculate posterior joint and conditional probabilities, probability distributions of population frequencies, and information-theoretic measures, by means of Bayesian nonparametric methods. Data imputation is automatic and done in a principled way. Markov-chain Monte Carlo calculations are automatically handled and do not require user supervision. Applications range from statistical estimation and probabilistic hypothesis testing to evidence-based inference and decision making, in a wide range of disciplines from astrophysics to medicine. For more details and examples see for instance Porta Mana et al. (2026) <doi:10.31219/osf.io/8nr56>, Dunson & Bhattacharya (2011) <doi:10.1093/acprof:oso/9780199694587.003.0005>, Lindley & Novick (1981) <doi:10.1214/aos/1176345331>, Bernardo & Smith (2000) <doi:10.1002/9780470316870>, Müller et al. (2015) <doi:10.1007/978-3-319-18968-0>. Requires the packages 'Nimble', 'parallel', 'extraDistr'.

PierGianLuca Porta Mana

prova

Nonparametric Probabilistic-Statistical Variate Analysis with
Automated Markov-Chain Monte Carlo

Aurora Grefsrud

Håkon Mydland

Maksim Ohvrill

Simen Hesthamar Hauge

util_prepPcheckpoints function

<dl><dt>x</dt>
<dd>Datapoints to be used for checking MCMC progress</dd>
<dt>auxmetadata</dt>
<dd>auxmetadata object</dd>
<dt>pointsid</dt>
<dd>Id of datapoints</dd></dl>

Arguments

Format datapoints used for MCMC monitoring — util_prepPcheckpoints

<dl>

<dt>x</dt>
<dd>Datapoints to be used for checking MCMC progress</dd>


<dt>auxmetadata</dt>
<dd>auxmetadata object</dd>


<dt>pointsid</dt>
<dd>Id of datapoints</dd>

</dl>

util_prepPcheckpoints: Format datapoints used for MCMC monitoring

Description

Usage

Value

Arguments