2019/01/13 by Ali Ghaderi, Ghaderi, Ali
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Probability (math.PR) #Statistical Mechanics and Entropy #Statistics Theory (math.ST) #cs.IT #math.IT #math.PR #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1901.03989
arxiv created 2019/01/13 · openalex publication_date 2019/01/13 · arxiv updated 2019/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We demonstrate that the functional form of the likelihood contains a sufficient amount of information for constructing a prior for the unknown parameters. We develop a four-step algorithm by invoking the information entropy as the measure of uncertainty and show how the information gained from coarse-graining and resolving power of the likelihood can be used to construct the likelihood-induced priors. As a consequence, we show that if the data model density belongs to the exponential family, the likelihood-induced prior is the conjugate prior to the corresponding likelihood.