estimators of divergence criteria for two normal distributions with bayesian approach

The use of statistical distributions for modeling‎, ‎specifically evaluating the similarity between two probability distributions using various divergence measures‎, ‎has recently attracted the attention of many of researchers to measure in context of machine learning‎. ‎given the importance of the topic‎, ‎this article introduces several the divergence criteria‎, ‎including kullback-leibler divergence‎, ‎total variation divergence‎, ‎alpha divergence‎, ‎and power divergence‎, ‎and computes the divergence parameters for two normal distributions‎. ‎the parameters are estimated using both maximum likelihood and bayesian methods‎. ‎in the bayesian approach‎, ‎a conjugate distribution is used as the prior‎, ‎taking into account the behavior of the parameters‎. ‎finally‎, ‎the estimation methods for two normal distributions are evaluated bsased on the mean square error criterion.