2000/08/07 by Ariel Caticha · 35 citations
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Applied mathematics #Artificial intelligence #Bayesian probability #Complex Systems and Time Series Analysis #Computer science #Conjugate prior #Covariant transformation #Entropy (arrow of time) #Inference #Joint quantum entropy #Mathematical physics #Mathematics #Maximum entropy probability distribution #Maximum entropy thermodynamics #Physics #Principle of maximum entropy #Prior probability #Statistical Mechanics and Entropy #Statistical physics #Statistics #Thermodynamics #cond-mat.stat-mech #math-ph #math.MP #math.ST #msc:54C70 #msc:62F15 #msc:82B03 #physics.data-an #stat.TH
paper · pdf · doi:10.1063/1.1381874
published in AIP conference proceedings 568, 94-105 (American Institute of Physics) · presented at MaxEnt 2000, the 20th International Workshop on Bayesian Inference and Maximum Entropy Methods (July 8-13, Gif-sur-Yvette, France))
arxiv created 2000/08/07 · openalex publication_date 2001/01/01 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The method of maximum entropy (ME) is extended to address the following problem: Once one accepts that the ME distribution is to be preferred over all others, the question is to what extent are distributions with lower entropy supposed to be ruled out. Two applications are given. The first is to the theory of thermodynamic fluctuations. The formulation is exact, covariant under changes of coordinates, and allows fluctuations of both the extensive and the conjugate intensive variables. The second application is to the construction of an objective prior for Bayesian inference. The prior obtained by following the ME method to its inevitable conclusion turns out to be a special case (α=1) of what are currently known under the name of entropic priors.