# A Symmetric Prior for Multinomial Probit Models

arXiv: https://arxiv.org/abs/1912.10334 · HTML: https://mlsanity.com/paper/1912.10334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Easy Variational Inference for Categorical Models via an Independent Binary Approximation](https://mlsanity.com/paper/2206.00093/) (2022)

## Abstract (arXiv)

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.