Principle of maximum entropy — This article is about the probability theoretic principle. For the classifier in machine learning, see maximum entropy classifier. For other uses, see maximum entropy (disambiguation). Bayesian statistics Theory Bayesian probability Probability… … Wikipedia
Principle of indifference — The principle of indifference (also called principle of insufficient reason) is a rule for assigning epistemic probabilities.Suppose that there are n gt; 1 mutually exclusive and collectively exhaustive possibilities.The principle of indifference … Wikipedia
Edwin Thompson Jaynes — Infobox Scientist name = E. T. Jaynes box width = 300px |200px image width = 200px caption = Edwin Thompson Jaynes (1922 1998), photo taken circa 1960. birth date = July 5, 1922 birth place = death date = April 30, 1998 death place = residence =… … Wikipedia
Prior probability — Bayesian statistics Theory Bayesian probability Probability interpretations Bayes theorem Bayes rule · Bayes factor Bayesian inference Bayesian network Prior · Posterior · Likelihood … Wikipedia
Digital physics — In physics and cosmology, digital physics is a collection of theoretical perspectives based on the premise that the universe is, at heart, describable by information, and is therefore computable. Therefore, the universe can be conceived as either … Wikipedia
Maximum entropy thermodynamics — In physics, maximum entropy thermodynamics (colloquially, MaxEnt thermodynamics) views equilibrium thermodynamics and statistical mechanics as inference processes. More specifically, MaxEnt applies inference techniques rooted in Shannon… … Wikipedia
Джейнс, Эдвин Томпсон — Эдвин Томпсон Джейнс Edwin Thompson Jaynes Дата рождения … Википедия
Copenhagen interpretation — Quantum mechanics Uncertainty principle … Wikipedia
Bayesian probability — Bayesian statistics Theory Bayesian probability Probability interpretations Bayes theorem Bayes rule · Bayes factor Bayesian inference Bayesian network Prior · Posterior · Likelihood … Wikipedia
Kullback–Leibler divergence — In probability theory and information theory, the Kullback–Leibler divergence[1][2][3] (also information divergence, information gain, relative entropy, or KLIC) is a non symmetric measure of the difference between two probability distributions P … Wikipedia
