The Logical Basis of Bayesian Reasoning and Its Application on Judicial Judgment
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DOI: 10.25236/iwass.2018.144
Corresponding Author
Juan Liu
Abstract
Bayesian inference is a law that corrects subjective judgments of related probabilities based on observed phenomena. The logical basis is that when the sample's capacity is close to the population, the probability of occurrence of events in the sample is close to the probability of occurrence of the population. The basic expression is: posterior probability = prior probability × standard similarity. Bayesian networks are applications of Bayesian inference, including directed acyclic graphs (DAGs) and conditional probability tables (CPTs) between nodes. Using the Bayesian programming tool to construct the Bayesian network, the ECHO model is used to analyze the node structure of the proposition in the first trial of von Blo, and the jury can be simulated by the insertion of the probability value in the judgment of the jury in the first instance, but find and set The difficulty of all conditional probabilities limits the effectiveness of its display of causal structures.
Keywords
Bayesian Reasoning, Bayesian Network, Judicial Referee