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conditionally independent

См. также в других словарях:

  • Independent Catholic Churches — are Christian denominations (or ) which claim apostolic succession for their bishops but are not a part of the Catholic Church, Oriental Orthodoxy, the Eastern Orthodox Churches, the Old Catholic Churches under the Archbishop of Utrecht or the… …   Wikipedia

  • Conditional independence — These are two examples illustrating conditional independence. Each cell represents a possible outcome. The events R, B and Y are represented by the areas shaded red, blue and yellow respectively. And the probabilities of these events are shaded… …   Wikipedia

  • Independence (probability theory) — In probability theory, to say that two events are independent intuitively means that the occurrence of one event makes it neither more nor less probable that the other occurs. For example: The event of getting a 6 the first time a die is rolled… …   Wikipedia

  • Statistical independence — In probability theory, to say that two events are independent, intuitively means that the occurrence of one event makes it neither more nor less probable that the other occurs. For example:* The event of getting a 6 the first time a die is rolled …   Wikipedia

  • De Finetti's theorem — In probability theory, de Finetti s theorem explains why exchangeable observations are conditionally independent given some (usually) unobservable quantity to which an epistemic probability distribution would then be assigned. It is named in… …   Wikipedia

  • de Finetti's theorem — In probability theory, de Finetti s theorem explains why exchangeable observations are conditionally independent given some latent variable to which an epistemic probability distribution would then be assigned. It is named in honor of Bruno de… …   Wikipedia

  • Markov random field — A Markov random field, Markov network or undirected graphical model is a set of variables having a Markov property described by an undirected graph. A Markov random field is similar to a Bayesian network in its representation of dependencies. It… …   Wikipedia

  • Joint probability distribution — In the study of probability, given two random variables X and Y that are defined on the same probability space, the joint distribution for X and Y defines the probability of events defined in terms of both X and Y. In the case of only two random… …   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

  • Kalman filter — Roles of the variables in the Kalman filter. (Larger image here) In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kálmán. Its purpose is to use measurements observed over time, containing noise (random variations)… …   Wikipedia

  • Co-training — is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998. Contents 1 …   Wikipedia

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