A Symmetric Prior for Multinomial Probit Models

Lane F. Burgette, David Puelz, P. Richard Hahn

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Fitted probabilities from widely used Bayesian multinomial probit models can depend strongly on the choice of a base category, which is used to uniquely identify the parameters of the model. This paper proposes a novel identification strategy, and associated prior distribution for the model parameters, that renders the prior symmetric with respect to relabeling the outcome categories. The new prior permits an efficient Gibbs algorithm that samples rank-deficient covariance matrices without resorting to Metropolis-Hastings updates.

Original languageEnglish (US)
Pages (from-to)991-1008
Number of pages18
JournalBayesian Analysis
Issue number3
StatePublished - 2021
Externally publishedYes


  • Gibbs sampler
  • base category
  • discrete choice
  • sum-to-zero identification

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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