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...aren Spärck Jones]] as a framework for [[Statistical model | probabilistic models]] to come. It is a formalism of [[information retrieval]] useful to derive ==Related models== ...

...of successes in 1 trial, either 0 or 1. The most common binary regression models are the [[logit model]] ([[logistic regression]]) and the [[probit model]] ...variable model]]s, together with a measurement model; or as probabilistic models, directly modeling the probability. ...

...Prime number|prime]]s in short intervals for which [[Cramér model|Cramér's probabilistic model of primes]] gives a wrong answer. ...t|Pintz|2007}} gave another proof, and also showed that most probabilistic models of primes incorrectly predict the [[mean square error]] ...

{{Short description|Probabilistic logic programming language}} | genre = [[Probabilistic logic]] ...

{{Short description|Probabilistic graphical models based on imprecise probability}} ...et]]s replace probability mass functions in the specification of the local models for the network variables given their parents. As a Bayesian network define ...

...andom groups''' are certain [[Group (mathematics)|group]]s obtained by a [[probabilistic]] construction. They were introduced by [[Mikhail Gromov (mathematician)|Mi ...babilistic model on the set of possible groups. Various such probabilistic models yield different (but related) notions of random groups. ...

...ministic.<ref>Beck & Melchers, p. 2202.</ref> In this case, one defines a probabilistic failure barrier surface, <math> \mathbf R (t)</math>, over the [[vector spa ...is included as part of the core modules of the [[NASA]]-designed [[NESSUS Probabilistic Analysis Software|NESSUS]] software.<ref>Shah ''et al.'', p. 5.</ref> It w ...

...ast2=Nowotarski|first2=Jakub|date=2016|title=A hybrid model for GEFCom2014 probabilistic electricity price forecasting|journal=International Journal of Forecasting| ...btaining accurate point predictions. Computing [[Probabilistic forecasting|probabilistic forecasts]], on the other hand, is generally a much more complex task and h ...

...such as methods using a probabilistic decision tree, a neural network or a probabilistic support-vector machine. Hence, for each variable <math>X_i</math> in domain ...ibutions. For each variable <math>X_i</math> in <math>\mathbf{X}</math>, a probabilistic decision tree is learned where <math>X_i</math> is the target variable and ...

...lar to commonly used supervised learning techniques, structured prediction models are typically trained by means of observed data in which the predicted valu ...m, A. |author3=Pereira, F. |title=Conditional random fields: Probabilistic models for segmenting and labeling sequence data|book-title =Proc. 18th Internatio ...

...under which a set of observed data is considered to be a realisation of a probabilistic or statistical model. However, in <math>\{ H_n(\theta) \}</math>, ''&theta; === Time Series Models === ...

...ly outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right<ref> Probabilistic classifiers generalize this notion of classifiers: instead of functions, th ...

{{Short description|Inference algorithm for probabilistic graphical models}} ...a simple and general [[Bayesian inference|exact inference]] algorithm in [[probabilistic graphical model]]s, such as [[Bayesian network]]s and [[Markov random field ...

...by [[Vladimir Vapnik|Vapnik]], but can be applied to other classification models.<ref name="Niculescu">{{cite conference |last1=Niculescu-Mizil |first1=Alex ...swer, but also a degree of certainty about the answer. Some classification models do not provide such a probability, or give poor probability estimates. ...

{{Short description|Cellular automaton with probabilistic rules}} '''Stochastic cellular automata''' or '''probabilistic cellular automata''' ('''PCA''') or '''random cellular automata''' or '''lo ...

...now" may obtain different interpretations, including [[Probabilistic logic|probabilistic]], [[Relational logic|relational]] and correlational, depending on the appl ...1=Geiger|first1=Dan|date=1990|title=Graphoids: A Qualitative Framework for Probabilistic Inference|format=PhD Dissertation, Technical Report R-142, Computer Science ...

...tion of probability to artificial intelligence appeared in his 1989 text ''Probabilistic Reasoning in Expert Systems: Theory and Algorithms''.<ref name="neoexpert" ...''.<ref name="neoexpert">{{cite book|last1=Neapolitan|first1=Richard|title=Probabilistic Reasoning in Expert Systems: Theory and Algorithms|date=1989|publisher=Wile ...

...ber 2012|url-status=dead}}</ref> The algorithm improves upon earlier topic models such as [[latent Dirichlet allocation]] (LDA) by modeling correlations betw | chapter = Pachinko allocation: DAG-structured mixture models of topic correlations ...

...="Bejenaro">{{cite journal|last = Bejenaro|first = G|title = Variations on probabilistic suffix trees: statistical modeling and prediction of protein families |jour ...taking values in a finite alphabet <math>A</math>, and characterized by a probabilistic context tree <math>(\tau,p)</math>, so that ...

{{Short description|Probabilistic programming language for Bayesian inference}} '''Stan''' is a [[probabilistic programming language]] for [[statistical inference]] written in [[C++]].<re ...