Conjugate Prior Distribution and Non-Informative Prior Distribution in Bayesian Statistics
This article provides an introduction to Bayesian statistics, focusing on the conjugate prior distribution and the non-informative prior distribution. The article explains the concept of the conjugate prior and its mathematical background through an example of the Beta distribution. It also discusses the use of the non-informative prior distribution, particularly the Beta(1,1) distribution, in cases where little or no prior knowledge is available. The article includes graphs that illustrate the effect of the likelihood function and observed data on the posterior distribution.
Tags: statistics, bayesianstatistics, machinelearning