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
        }
}
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

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