Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary Order

2020/08/02 by Clélia de Mulatier, Matteo Marsili, de Mulatier, Clélia +1 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Data Analysis #Data Analysis with R #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Quantitative Methods (q-bio.QM) #Statistical Mechanics and Entropy #Statistical and numerical algorithms #Statistics Theory (math.ST) #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2008.00520

openalex publication_date 2020/08/02 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

Compared to v1.0: changes structures of Nset/Kset from map to vector --> significantly faster. The rest of the code remains unchanged.

Cited by

• A job-based assessment of economic complexity: from hidden to revealed

Related

• Entropy-based Characterization of Modeling Constraints
• Description length of canonical and microcanonical models
• Efficient Bayesian Inference for Generalized Bradley-Terry Models
• Minimum Reduced-Order Models via Causal Inference
• Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models