- maximum-likelihood criterion
- стат. критерий максимального правдоподобия
Большой англо-русский и русско-английский словарь. 2001.
maximum-likelihood criterion
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Maximum likelihood sequence estimation — (MLSE) is a mathematical algorithm to extract useful data out of a noisy data stream. Contents 1 Theory 2 Background 3 References 4 Further reading … Wikipedia
Maximum-Likelihood-Methode — Die Maximum Likelihood Methode (von engl. maximale Wahrscheinlichkeit) bezeichnet in der Statistik ein parametrisches Schätzverfahren. Dabei wird vereinfacht so vorgegangen, dass derjenige Parameter als Schätzung ausgewählt wird, gemäß dessen… … Deutsch Wikipedia
Maximum parsimony — Maximum parsimony, often simply referred to as parsimony, is a non parametric statistical method commonly used in computational phylogenetics for estimating phylogenies. Under maximum parsimony, the preferred phylogenetic tree is the tree that… … Wikipedia
Maximum parsimony (phylogenetics) — Parsimony is a non parametric statistical method commonly used in computational phylogenetics for estimating phylogenies. Under parsimony, the preferred phylogenetic tree is the tree that requires the least evolutionary change to explain some… … Wikipedia
Likelihood function — In statistics, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model, defined as follows: the likelihood of a set of parameter values given some observed outcomes is equal to the probability of … Wikipedia
Akaike Information Criterion — Ein Informationskriterium ist ein Kriterium zur Auswahl eines Modells in der angewandten Statistik bzw. der Ökonometrie. Dabei gehen die Anpassungsgüte des geschätzten Modells an die vorliegenden empirischen Daten (Stichprobe) und Komplexität des … Deutsch Wikipedia
Bayesian information criterion — In statistics, in order to describe a particular dataset, one can use non parametric methods or parametric methods. In parametric methods, there might be various candidate models with different number of parameters to represent a dataset. The… … Wikipedia
Optimality criterion — In statistics, an optimality criterion provides a measure of the fit of the data to a given hypothesis. The selection process is determined by the solution that optimizes the criteria used to evaluate the alternative hypotheses. The term has been … Wikipedia
Akaike information criterion — Akaike s information criterion, developed by Hirotsugu Akaike under the name of an information criterion (AIC) in 1971 and proposed in Akaike (1974), is a measure of the goodness of fit of an estimated statistical model. It is grounded in the… … Wikipedia
Principle of maximum entropy — This article is about the probability theoretic principle. For the classifier in machine learning, see maximum entropy classifier. For other uses, see maximum entropy (disambiguation). Bayesian statistics Theory Bayesian probability Probability… … Wikipedia
Deviance information criterion — The deviance information criterion (DIC) is a hierarchical modeling generalization of the AIC (Akaike information criterion) and BIC (Bayesian information criterion, also known as the Schwarz criterion). It is particularly useful in Bayesian… … Wikipedia
