BID Modeling vs STAN: 8-Candidate Polling Benchmark

This benchmark evaluates Discretized Bayesian Induction (BID / BIDSim) against Continuous MCMC (Stan / NUTS) on an 8-candidate political opinion polling model. Both methods incorporate empirical historical baselines via Maximum Entropy (Maxent) priors and evaluate candidate vote shares under strict probability conservation.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-09
DOI
https://doi.org/10.5281/zenodo.24081412
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
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article

BID Modeling vs STAN: 8-Candidate Polling Benchmark

Bernard Pargamin
Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
article

BID Modeling vs STAN: 8-Candidate Polling Benchmark

Bernard Pargamin
article en

Abstract

This benchmark evaluates Discretized Bayesian Induction (BID / BIDSim) against Continuous MCMC (Stan / NUTS) on an 8-candidate political opinion polling model. Both methods incorporate empirical historical baselines via Maximum Entropy (Maxent) priors and evaluate candidate vote shares under strict probability conservation.

Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
DOIOpen Access PDFOpenAlex
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BID Modeling vs STAN: 8-Candidate Polling Benchmark — Bernard Pargamin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS