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  2. Maximum a posteriori estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_a_posteriori...

    An estimation procedure that is often claimed to be part of Bayesian statistics is the maximum a posteriori (MAP) estimate of an unknown quantity, that equals the mode of the posterior density with respect to some reference measure, typically the Lebesgue measure.

  3. Posterior probability - Wikipedia

    en.wikipedia.org/wiki/Posterior_probability

    From a given posterior distribution, various point and interval estimates can be derived, such as the maximum a posteriori (MAP) or the highest posterior density interval (HPDI). [4] But while conceptually simple, the posterior distribution is generally not tractable and therefore needs to be either analytically or numerically approximated. [5]

  4. Viterbi algorithm - Wikipedia

    en.wikipedia.org/wiki/Viterbi_algorithm

    The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events.

  5. Bayesian statistics - Wikipedia

    en.wikipedia.org/wiki/Bayesian_statistics

    The maximum a posteriori, which is the mode of the posterior and is often computed in Bayesian statistics using mathematical optimization methods, remains the same. The posterior can be approximated even without computing the exact value of P ( B ) {\displaystyle P(B)} with methods such as Markov chain Monte Carlo or variational Bayesian methods .

  6. Laplace's approximation - Wikipedia

    en.wikipedia.org/wiki/Laplace's_approximation

    where ^ is the location of a mode of the joint target density, also known as the maximum a posteriori or MAP point and is the positive definite matrix of second derivatives of the negative log joint target density at the mode = ^. Thus, the Gaussian approximation matches the value and the log-curvature of the un-normalised target density at the ...

  7. Bayes estimator - Wikipedia

    en.wikipedia.org/wiki/Bayes_estimator

    In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss).

  8. ‘12 Badass Women’ by Huffington Post

    testkitchen.huffingtonpost.com/badass-women/?ir=...

    The Huffington Post reached out to historians across the country to create a list of women who deserve more recognition for their accomplishments.

  9. Posterior predictive distribution - Wikipedia

    en.wikipedia.org/wiki/Posterior_predictive...

    In Bayesian statistics, the posterior predictive distribution is the distribution of possible unobserved values conditional on the observed values. [1] [2]Given a set of N i.i.d. observations = {, …,}, a new value ~ will be drawn from a distribution that depends on a parameter , where is the parameter space.

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