Bayesian algorithm


Bayesian algorithm
бейесовский алгоритм

Авиасловарь. . 2004.

Смотреть что такое "Bayesian algorithm" в других словарях:

  • Bayesian algorithm — …   Википедия

  • Bayesian inference in phylogeny — generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likelihood of the data, generated by a multiple alignment. The Bayesian approach has… …   Wikipedia

  • Bayesian inference — is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name Bayesian comes from the frequent use of Bayes theorem in the inference process. Bayes theorem… …   Wikipedia

  • Bayesian additive regression kernels — (BARK) is a non parametric statistics model for regression and classificationcite web| title= Bayesian Additive Regression Kernels |url= http://stat.duke.edu/people/theses/OuyangZ.html |Author = Zhi Ouyang |Publisher = Duke University] . The… …   Wikipedia

  • Bayesian network — A Bayesian network, Bayes network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example …   Wikipedia

  • Bayesian model comparison — A common problem in statistical inference is to use data to decide between two or more competing models. Frequentist statistics uses hypothesis tests for this purpose. There are several Bayesian approaches. One approach is through Bayes… …   Wikipedia

  • Expectation-maximization algorithm — An expectation maximization (EM) algorithm is used in statistics for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. EM alternates between performing an… …   Wikipedia

  • Nested sampling algorithm — The nested sampling algorithm is a computational approach to the problem of comparing models in Bayesian statistics, developed in 2004 by physicist John Skilling.[1] Contents 1 Background 2 Applications 3 …   Wikipedia

  • Metropolis–Hastings algorithm — The Proposal distribution Q proposes the next point that the random walk might move to. In mathematics and physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo method for obtaining a sequence of random samples from a… …   Wikipedia

  • Variational Bayesian methods — Variational Bayesian methods, also called ensemble learning, are a family of techniques for approximating intractable integrals arising in Bayesian statistics and machine learning. They can be used to lower bound the marginal likelihood (i.e.… …   Wikipedia

  • Gauss–Newton algorithm — The Gauss–Newton algorithm is a method used to solve non linear least squares problems. It can be seen as a modification of Newton s method for finding a minimum of a function. Unlike Newton s method, the Gauss–Newton algorithm can only be used… …   Wikipedia

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