ArticleJournal of mathematical psychology2019
Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference.
Article in Journal of mathematical psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- When the wild things are: Defining mammalian diel activity and plasticity.Science advances · 2025Article
- Discourse on measurement.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Refining Bayesian hierarchical MPT modeling: Integrating prior knowledge and ordinal expectations.Behavior research methods · 2024Article
- Multibridge: an R package to evaluate informed hypotheses in binomial and multinomial models.Behavior research methods · 2023Article
- Toward a more comprehensive modeling of sequential lineups.Cognitive research: principles and implications · 2022Article
- Cognitive Aging and Tests of Rationality.The Spanish journal of psychology · 2019Article
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2 authors.
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Abstract
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 alternative sampling methods for estimating Bayes factors for these model representations using the encompassing Bayes factor method. We introduce the R package multinomineq, which provides an easily-accessible interface to a computationally efficient implementation of these techniques.
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