Bayesian Analysis

Spatio-Temporal Modeling of Legislation and Votes

Eric Wang, Esther Salazar, David Dunson, and Lawrence Carin

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A model is presented for analysis of multivariate binary data with spatio-temporal dependencies, and applied to congressional roll call data from the United States House of Representatives and Senate. The model considers each legislator’s constituency (location), the congressional session (time) of each vote, and the details (text) of each piece of legislation. The model can predict votes of new legislation from only text, while imposing smooth temporal evolution of legislator latent features, and correlation of legislators with adjacent constituencies. Additionally, the model estimates the number of latent dimensions required to represent the data. A Gibbs sampler is developed for posterior inference. The model is demonstrated as an exploratory tool of legislation and it performs well in quantitative comparisons to a traditional ideal-point model.

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Bayesian Anal., Volume 8, Number 1 (2013), 233-268.

First available in Project Euclid: 4 March 2013

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Factor analysis Indian buffet process latent Dirichlet allocation political science topic modeling


Wang, Eric; Salazar, Esther; Dunson, David; Carin, Lawrence. Spatio-Temporal Modeling of Legislation and Votes. Bayesian Anal. 8 (2013), no. 1, 233--268. doi:10.1214/13-BA810.

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