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Faster Ideal Point Estimates with Less Wait for Political Scientists

ideal pointexpectation maximization algorithmmassive datavariational inferenceMethodology@APSR140 R files24 datasetsDataverse
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Ideal points help explain voting and policy preferences. New methods tackle massive data challenges quickly.

➡️ New Fast Methods

  • Standard EM algorithm extended to handle different outcome types (binary, ordinal, continuous)
  • Added variational EM algorithms for dynamic/hierarchical ideal point models

➡️ Computational Advantages

  • Produces identical estimates as standard MCMC but in minutes instead of days
  • Works with various data sources including roll calls, surveys and social media text

➡️ Open-source Implementation Available

Article card for article: Fast Estimation of Ideal Points With Massive Data
Fast Estimation of Ideal Points With Massive Data was authored by Kosuke Imai, James Lo and Jonathan Olmsted. It was published by Cambridge in APSR in 2016.
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