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Дата изменения: Unknown Дата индексирования: Mon Oct 1 21:27:32 2012 Кодировка: Поисковые слова: п п п п п п п |
Under Construction!
BF = P(M_2|X_n)/P(M_1|X_n) * P(M_1)/P(M_2),where M_i (i=1,2) indicates models/distributions, P(M_1)/P(M_2) is the prior odds, and P(M_2|X_n)/P(M_1|X_n) is the posterior odds.
p(MD) = p(M|D) p(D) = p(M) p(D|M), p(M|D) = p(M) p(D|M) / p(D).This is Bayes' Theorem. Usually, M represents the model parameters, D represents the data, and the | symbol indicates a statement of conditional probability. Here, p(M) are the a priori probabilities on the model parameters, p(D|M) is the likelihood, and p(D) is a normalizing factor.
E(X_{n+1}|X_1,...,X_n)= X_n,where X_1,...,X_n are a sequence of random variables. In other words, the conditional expectation of X_{n+1}, given with all past observations, only depends on the immediate previous observation.
counts ∼ Po(lambda)
flux ∼ N(mean,stddev)