2009/11/11 by PierGianLuca Porta Mana, P. G. L. Porta Mana, Mana, P. G. L. Porta · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #03B48 #60A05 #60G09 #Data Analysis #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Information Theory (cs.IT) #Probability (math.PR) #Statistical Mechanics and Entropy #Statistics and Probability (physics.data-an) #cs.IT #math.IT #math.PR #msc:03B48 #msc:60A05 #msc:60G09 #physics.data-an
paper · pdf · doi:10.48550/arxiv.0911.2197
24 pages of main text and references, 8 pages of tables, 7 pages of additional references
arxiv created 2009/11/11 · openalex publication_date 2009/11/11 · arxiv updated 2016/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
What is the relationship between plausibility logic and the principle of maximum entropy? When does the principle give unreasonable or wrong results? When is it appropriate to use the rule `expectation = average'? Can plausibility logic give the same answers as the principle, and better answers if those of the principle are unreasonable? To try to answer these questions, this study offers a numerical collection of plausibility distributions given by the maximum-entropy principle and by plausibility logic for a set of fifteen simple problems: throwing dice.