Standard Bayesian multinomial probit (MNP) models that are fit using different base categories can give different predictions. Therefore, we propose the symmetric MNP model, which does not make reference to a base category. To achieve this, we employ novel sum-to-zero identifying restrictions on the latent utilities and regression coefficients that define the model. This results in a model whos...

Bayesian classification commonly relies on probit models, with data augmentation algorithms used for posterior computation. By imputing latent Gaussian variables, one can often trivially adapt computational approaches used in Gaussian models. However, MCMC for multinomial probit (MNP) models can be inefficient in practice due to high posterior dependence between latent variables and parameters,...

On the other hand, path choice is a very complex context to be simulated because of the above mentioned large number of alternative paths and of their overlapping which, within the random utility (RU) theory, means correlations among utilities. This complexity suggests the use of demanding models (like the Probit model) without a closed form which require simulation methods, thus inducing high ...

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Theory: This paper reformulates diversionary theory to take into account the effect of domestic structures on the propensity of leaders to use foreign policy to manipulate domestic politics. Hypotheses: The structure of domestic political institutions and levels of policy resources condition the willingness of leaders to use conflict involvement to manipulate domestic audiences. Method: Probit ...

In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance ga...