UPG.Diag.Binomial

<p>an object of class <code>UPG.Binomial</code>.</p>

object

<p><code>UPG.Diag.Binomial</code> computes inefficiency factors, effective sample size and effective sampling rate based on the posterior distributions in an <code>UPG.Binomial</code> object.</p>

Highly efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and boosting algorithms outlined in Fr<fc>hwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>. The underlying implementation is written in C++.

Gregor Zens

Efficient Bayesian Models for Binary and Categorical Data

UPG.Diag.Binomial: MCMC Diagnostics for `UPG.Binomial` objects

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