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Found 3 papers, 2 papers with code
Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference
Many psychological theories can be operationalized as linear inequality constraints on the parameters of multinomial distributions (e. g., discrete choice analysis).
Computation Methodology
Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation
The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection.
Quantifying Uncertainty in Transdimensional Markov Chain Monte Carlo Using Discrete Markov Models
To facilitate this evaluation, transdimensional Markov chain Monte Carlo (MCMC) relies on sampling a discrete indexing variable to estimate the posterior model probabilities.
Methodology Computation
