# Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference.

@article{Heck2018MultinomialMW, title={Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference.}, author={Daniel W Heck and Clintin P. Davis-Stober}, journal={Journal of mathematical psychology}, year={2018}, volume={91}, pages={ 70-87 } }

- Published in Journal of mathematical psychology 2018
DOI:10.1016/j.jmp.2019.03.004

Many psychological theories can be operationalized as linear inequality constraints on the parameters of multinomial distributions (e.g., discrete choice analysis). These constraints can be described in two equivalent ways: Either as the solution set to a system of linear inequalities or as the convex hull of a set of extremal points (vertices). For both representations, we describe a general Gibbs sampler for drawing posterior samples in order to carry out Bayesian analyses. We also summarize… CONTINUE READING