Maximum Probability and Relative Entropy Maximization. Bayesian Maximum Probability and Empirical Likelihood
نویسنده
چکیده
Works, briefly surveyed here, are concerned with two basic methods: Maximum Probability and Bayesian Maximum Probability; as well as with their asymptotic instances: Relative Entropy Maximization and Maximum Non-parametric Likelihood. Parametric and empirical extensions of the latter methods – Empirical Maximum Maximum Entropy and Empirical Likelihood – are also mentioned. The methods are viewed as tools for solving certain ill-posed inverse problems, called Π-problem, Φ-problem, respectively. Within the two classes of problems, probabilistic justification and interpretation of the respective methods are discussed.
منابع مشابه
Maximum Probability and Maximum Entropy methods: Bayesian interpretation
(Jaynes’) Method of (Shannon-Kullback’s) Relative Entropy Maximization (REM or MaxEnt) can be at least in the discrete case according to the Maximum Probability Theorem (MPT) viewed as an asymptotic instance of the Maximum Probability method (MaxProb). A simple bayesian interpretation of MaxProb is given here. MPT carries the interpretation over into REM.
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