Bayesian Probability Estimation for Reasoning Process

ABSTRACT: It is a comprehensible fact that people always desire to be able to remove or at least to decrease the level of uncertainty in real world application. In all the areas of science and technology, it is important to have an accurate measurement for evaluating the uncertainty. Increasing accuracy of measurement includes the identification, analysis and minimization of errors, compute and estimate the result of uncertainties. A probability is the branch of science studying the quantitative inferences of uncertainty. Probability is involved in various fields such as finance, meteorology, engineering, medicine, management etc. In this thesis, Bayesian probability estimation for reasoning process is analyzed. The conditional, joint, prior, and posterior probabilities are mentioned. The importance of the probability views based on the subjectivity and objectivity, and the properties of these two terms are considered. The Bayesian inference and the generalized Bayes’ theorem are discussed. Keywords: Uncertainty, Bayesian method, subjective and objective probabilities, Bayesian inference, generalized Bayes’ theorem. …………………………………………………………………………………………………………………………