Parameter inference in a probabilistic model from data: Regulation of transition rate in the Monte Carlo method
| Autor: | Hirohito Kiwata |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Inverse-chi-squared distribution Mathematical optimization Monte Carlo method Condensed Matter Physics 01 natural sciences Normal-gamma distribution Symmetric probability distribution 03 medical and health sciences 0302 clinical medicine Joint probability distribution 0103 physical sciences Probability mass function Probability distribution Applied mathematics 010306 general physics Compound probability distribution 030217 neurology & neurosurgery Mathematics |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 491:1014-1022 |
| ISSN: | 0378-4371 |
| DOI: | 10.1016/j.physa.2017.09.103 |
| Popis: | We consider the inference of parameters in a probabilistic model from a data set, which is generated by an unknown probabilistic model. The Monte Carlo method is a tool for obtaining a data set obeying a given probability distribution. A set of transition rates is required to satisfy three conditions (irreducible, aperiodic, and stationary) for a sampled data set to represent a probability distribution. We utilize the stationary condition of a probability distribution with respect to transition rates to infer parameters. A frequency distribution by a data set substitutes for an unknown probability distribution in the condition. Our method includes minimum probability flow as a special case and becomes superior to it as the number of samples increases. |
| Databáze: | OpenAIRE |
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