- Bayesian algorithm
- бейесовский алгоритм
Авиасловарь. М.А.Левин. 2004.
Авиасловарь. М.А.Левин. 2004.
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