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Bayesian Inference of Multiple Ising Models for Heterogeneous Public Opinion Survey Networks by Avalos-Pacheco, Alejandra; Lazzerini, Andrea; Lupparelli, Monia; Stingo, Francesco C. is a scholarly article available to read on EtoBox.

What is Bayesian Inference of Multiple Ising Models for Heterogeneous Public Opinion Survey Networks about?

In public opinion studies, the relationships between opinions on different topics are likely to shift based on the characteristics of the respondents. Thus, understanding the complexities of public opinion requires methods that can account for the heterogeneity in responses across different groups. Multiple graphs are used to study how external factors-such as time spent online or generational differences-shape the joint dependence relationships between opinions on various topics. Specifically, we propose a class of multiple Ising models where a set of graphs across different groups are able to capture these variations and to model the heterogeneity induced in a set of binary variables by external factors. The proposed Bayesian methodology is based on a Markov Random Field prior for the multiple graph setting. Such prior enables the borrowing of strength across the different groups to encourage common edges when supported by the data. Sparse inducing spike-and-slab priors are employed on the parameters that measure graph similarities to learn which subgroups have a shared graph structure. Two Bayesian approaches are developed for the inference of multiple Ising models with a specia

Author

Avalos-Pacheco, Alejandra; Lazzerini, Andrea; Lupparelli, Monia; Stingo, Francesco C.

Published

2024

Language

EN