maximum-likelihood criterion
1Maximum 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 …
2Maximum-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… …
3Maximum 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… …
4Maximum 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… …
5Likelihood 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 …
6Akaike 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 …
7Bayesian 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… …
8Optimality 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 …
9Akaike 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… …
10Principle 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… …
