Beta Likelihood Estimation and Its Application to Specify Prior Probabilities in Bayesian Network
Journal of Advances in Mathematics and Computer Science · pp. 1–21 · Published 27 Apr 2016
10.9734/BJMCS/2016/25731Abstract
Maximum likelihood estimation (MLE) is a popular technique of statistical parameter estimation. When random variable conforms beta distribution, the research focuses on applying MLE into beta density function. This method is called beta likelihood estimation, which results out useful estimation equations. It is easy to calculate statistical estimates based on these equations in case that parameters of beta distribution are positive integer numbers. Essentially, the method takes advantages of interesting features of functions gamma, digamma, and trigamma. An application of beta likelihood estimation is to specify prior probabilities in Bayesian network.
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