@prefix rdf: . @prefix dbpedia: . @prefix ns2: . dbpedia:Bayesian_network rdf:type ns2:Networks . @prefix opencyc: . dbpedia:Bayesian_network rdf:type opencyc:Mx4rg_a2gGKPQdiS9dYSV7YWxg . @prefix dbpprop: . dbpedia:Bayesian_network dbpprop:name "Artificial intelligence"@en , "Statistics"@en . @prefix owl: . dbpedia:Bayesian_network owl:sameAs opencyc:Mx4rvhSzKJwpEbGdrcN5Y29ycA , . @prefix ns6: . dbpedia:Bayesian_network owl:sameAs ns6:Mx4rvhSzKJwpEbGdrcN5Y29ycA . @prefix foaf: . @prefix ns8: . dbpedia:Bayesian_network foaf:page ns8:Bayesian_network ; dbpprop:reference , , , , . @prefix ns9: . dbpedia:Bayesian_network dbpprop:reference ns9:rubrique6 , , , , , , , , . @prefix rdfs: . dbpedia:Bayesian_network rdfs:label "\u30D9\u30A4\u30B8\u30A2\u30F3\u30CD\u30C3\u30C8\u30EF\u30FC\u30AF"@ja , "\u0411\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0430\u044F \u0441\u0435\u0442\u044C \u0434\u043E\u0432\u0435\u0440\u0438\u044F"@ru , "Re\u0163ea bayesian\u0103"@ro , "Reti Bayesiane"@it , "Sie\u0107 bayesowska"@pl , "Bayes'sches Netz"@de , "R\u00E9seau bay\u00E9sien"@fr , "Bayesian network"@en , "Rede Bayesiana"@pt , "Probabilistisch netwerk"@nl , "Bayesverkko"@fi , "Red bayesiana"@es ; dbpprop:abstract "Sie\u0107 bayesowska s\u0142u\u017Cy do przedstawiania zale\u017Cno\u015Bci pomi\u0119dzy zdarzeniami bazuj\u0105c na rachunku prawdopodobie\u0144stwa. Klasycznym przyk\u0142adem jest reprezentowanie zale\u017Cno\u015Bci pomi\u0119dzy symptomami a chorob\u0105. Formalnie taka sie\u0107 jest modelowana za pomoc\u0105 skierowanego grafu acyklicznego, w kt\u00F3rym wierzcho\u0142ki reprezentuj\u0105 zdarzenia, a \u0142uki zwi\u0105zki przyczynowe pomi\u0119dzy tymi zdarzeniami. Je\u015Bli od wierzcho\u0142ka A prowadzi \u015Bcie\u017Cka do wierzcho\u0142ka B to B jest potomkiem A. Podstawowym za\u0142o\u017Ceniem sieci bayesowskiej jest niezale\u017Cno\u015B\u0107 danego zdarzenia od wszystkich innych, kt\u00F3re nie s\u0105 jego potomkami."@pl , "\u30D9\u30A4\u30B8\u30A2\u30F3\u30CD\u30C3\u30C8\u30EF\u30FC\u30AF(Bayesian Network)\u306F\u3001\u56E0\u679C\u95A2\u4FC2\u3092\u78BA\u7387\u306B\u3088\u308A\u8A18\u8FF0\u3059\u308B\u30B0\u30E9\u30D5\u30A3\u30AB\u30EB\u30E2\u30C7\u30EB\u306E\u4E00\u3064\u3067\u3001\u8907\u96D1\u306A\u56E0\u679C\u95A2\u4FC2\u306E\u63A8\u8AD6\u3092\u6709\u5411\u30B0\u30E9\u30D5\u69CB\u9020\u306B\u3088\u308A\u8868\u3059\u3068\u3068\u3082\u306B\u3001\u500B\u3005\u306E\u5909\u6570\u306E\u95A2\u4FC2\u3092\u6761\u4EF6\u3064\u304D\u78BA\u7387\u3067\u8868\u3059\u78BA\u7387\u63A8\u8AD6\u306E\u30E2\u30C7\u30EB\u3067\u3042\u308B\u3002 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\u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u0445 \u0438 \u0438\u0445 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u044B\u0445 \u0437\u0430\u0432\u0438\u0441\u0438\u043C\u043E\u0441\u0442\u0435\u0439. \u041D\u0430\u043F\u0440\u0438\u043C\u0435\u0440, \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0430\u044F \u0441\u0435\u0442\u044C \u043C\u043E\u0436\u0435\u0442 \u0431\u044B\u0442\u044C \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u043E\u0432\u0430\u043D\u0430 \u0434\u043B\u044F \u0432\u044B\u0447\u0438\u0441\u043B\u0435\u043D\u0438\u044F \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u0438 \u0442\u043E\u0433\u043E, \u0447\u0435\u043C \u0431\u043E\u043B\u0435\u043D \u043F\u0430\u0446\u0438\u0435\u043D\u0442 \u043F\u043E \u043D\u0430\u043B\u0438\u0447\u0438\u044E \u0438\u043B\u0438 \u043E\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u044E \u0440\u044F\u0434\u0430 \u0441\u0438\u043C\u043F\u0442\u043E\u043C\u043E\u0432, \u043E\u0441\u043D\u043E\u0432\u044B\u0432\u0430\u044F\u0441\u044C \u043D\u0430 \u0434\u0430\u043D\u043D\u044B\u0445 \u043E \u0437\u0430\u0432\u0438\u0441\u0438\u043C\u043E\u0441\u0442\u0438 \u043C\u0435\u0436\u0434\u0443 \u0441\u0438\u043C\u043F\u0442\u043E\u043C\u0430\u043C\u0438 \u0438 \u0431\u043E\u043B\u0435\u0437\u043D\u044F\u043C\u0438. \u0424\u043E\u0440\u043C\u0430\u043B\u044C\u043D\u043E, \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0430\u044F \u0441\u0435\u0442\u044C \u2014 \u044D\u0442\u043E \u043D\u0430\u043F\u0440\u0430\u0432\u043B\u0435\u043D\u043D\u044B\u0439 \u0430\u0446\u0438\u043A\u043B\u0438\u0447\u0435\u0441\u043A\u0438\u0439 \u0433\u0440\u0430\u0444, \u0432\u0435\u0440\u0448\u0438\u043D\u044B \u043A\u043E\u0442\u043E\u0440\u043E\u0433\u043E \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u044F\u044E\u0442 \u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u0435, \u0430 \u0440\u0435\u0431\u0440\u0430 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\u044D\u0444\u0444\u0435\u043A\u0442\u0438\u0432\u043D\u044B\u0435 \u043C\u0435\u0442\u043E\u0434\u044B, \u043A\u043E\u0442\u043E\u0440\u044B\u0435 \u0438\u0441\u043F\u043E\u043B\u044C\u0437\u0443\u044E\u0442\u0441\u044F \u0434\u043B\u044F \u0432\u044B\u0447\u0438\u0441\u043B\u0435\u043D\u0438\u0439 \u0438 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u044F \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0438\u0445 \u0441\u0435\u0442\u0435\u0439. \u0411\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0438\u0435 \u0441\u0435\u0442\u0438, \u043A\u043E\u0442\u043E\u0440\u044B\u0435 \u043C\u043E\u0434\u0435\u043B\u0438\u0440\u0443\u044E\u0442 \u043F\u043E\u0441\u043B\u0435\u0434\u043E\u0432\u0430\u0442\u0435\u043B\u044C\u043D\u043E\u0441\u0442\u0438 \u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u0445, \u043D\u0430\u0437\u044B\u0432\u0430\u044E\u0442 \u0434\u0438\u043D\u0430\u043C\u0438\u0447\u0435\u0441\u043A\u0438\u043C\u0438 \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0438\u043C\u0438 \u0441\u0435\u0442\u044F\u043C\u0438. \u0411\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0438\u0435 \u0441\u0435\u0442\u0438, \u0432 \u043A\u043E\u0442\u043E\u0440\u044B\u0445 \u043C\u043E\u0433\u0443\u0442 \u043F\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u043E\u0432\u0430\u0442\u044C \u043A\u0430\u043A \u0434\u0438\u0441\u043A\u0440\u0435\u0442\u043D\u044B\u0435 \u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u0435, \u0442\u0430\u043A \u0438 \u043D\u0435\u043F\u0440\u0435\u0440\u044B\u0432\u043D\u044B\u0435, \u043D\u0430\u0437\u044B\u0432\u0430\u044E\u0442\u0441\u044F \u0433\u0438\u0431\u0440\u0438\u0434\u043D\u044B\u043C\u0438 \u0431\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0438\u043C\u0438 \u0441\u0435\u0442\u044F\u043C\u0438."@ru , "Una red bayesiana, o red de creencia, es un modelo probabil\u00EDstico multivariado que relaciona un conjunto de variables aleatorias mediante un grafo dirigido que indica expl\u00EDcitamente influencia causal. Gracias a su motor de actualizaci\u00F3n de probabilidades, el Teorema de Bayes, las redes bayesianas son una herramienta extremadamente \u00FAtil en la estimaci\u00F3n de probabilidades ante nuevas evidencias. Una red bayesiana es un tipo de red causal. Un h\u00EDbrido de red bayesiana y Teor\u00EDa de la Utilidad es un diagrama de influencia."@es , "Modelele grafice probabilistice sunt grafuri \u00EEn care nodurile reprezint\u0103 variabile aleatoare, iar arcele (respectiv lipsa acestora) reprezint\u0103 presupuneri de independen\u0163\u0103 condi\u0163ionat\u0103. Ca urmare, ele ofer\u0103 o reprezentare compact\u0103 a distribu\u0163iilor probabilistice cumulate. Modelele grafice neorientate, numite \u015Fi C\u00E2mpuri Markov Aleatoare (Markov Random Fields) sau Re\u0163ele Markov, au o defini\u0163ie simpl\u0103 a independen\u0163ei: dou\u0103 (seturi de) noduri A \u015Fi B sunt condi\u0163ional independente dac\u0103, dat fiind un al treilea (set) C, toate c\u0103ile dintre nodurile A \u015Fi B sunt separate de un nod din C. Spre deosebire, modelele grafice orientate (numite \u015Fi Re\u0163ele Bayesiene), au o no\u0163iune mai complex\u0103 a independen\u0163ei, care ia \u00EEn considerare direc\u0163ia arcelor; acestea au mai multe avantaje: cel mai important este c\u0103 un arc de la A la B poate fi interpretat ca A \"cauzeaz\u0103\" pe B. Aceasta poate fi folosit\u0103 ca \"ghid\" pentru construirea grafului. \u00CEn plus, modelele orientate pot codifica rela\u0163ii deterministe \u015Fi sunt mai u\u015For de \u00EEnv\u0103\u0163at. Surs\u0103 (traducere):"@ro , "As Redes Bayesianas foram desenvolvidas in\u00EDcio dos anos 80 para facilitar a tarefa de predi\u00E7\u00E3o e \u201Cabdu\u00E7\u00E3o\u201D em sistemas de Intelig\u00EAncia Artificial (AI) (Pearl, 2000). Em resumo, Redes Bayesianas (RB) tamb\u00E9m conhecidas como redes de opini\u00E3o, redes causais, gr\u00E1ficos de depend\u00EAncia probabil\u00EDstica, s\u00E3o modelos gr\u00E1ficos para racioc\u00EDnio (conclus\u00F5es) baseado na incerteza, onde os n\u00F3s representam as vari\u00E1veis (discreta ou cont\u00EDnua), e os arcos representam a conex\u00E3o direta entre eles . Ela vem se tornando a metodologia padr\u00E3o para a constru\u00E7\u00E3o dos sistemas que confiam no conhecimento probabil\u00EDstico e tem sido aplicada em uma variedade de atividades do mundo real. Redes Bayesianas s\u00E3o modelos de representa\u00E7\u00E3o do conhecimento que trabalham com o conhecimento incerto e incompleto atrav\u00E9s da Teoria da Probabilidade Bayesiana, publicada pelo matem\u00E1tico Thomas Bayes em 1763. Matematicamente, uma Rede Bayesiana \u00E9 uma representa\u00E7\u00E3o compacta de uma tabela de conjun\u00E7\u00E3o de probabilidades do universo do problema. Por outro lado, do ponto de vista de um especialista, Redes Bayesianas constituem um modelo gr\u00E1fico que representa de forma simples as rela\u00E7\u00F5es de causalidade das vari\u00E1veis de um sistema. Essa representa\u00E7\u00E3o tem como uma das suas principais caracter\u00EDsticas a adaptabilidade, podendo, a partir de novas informa\u00E7\u00F5es, e com base em informa\u00E7\u00F5es de cunho verdadeiro, gerar altera\u00E7\u00F5es nas depend\u00EAncias e nos seus conceitos. Permite, dessa forma, que as probabilidades n\u00E3o sejam meros acasos, podendo confirmar e criar novos conceitos. A representa\u00E7\u00E3o da Rede Bayesiana \u00E9 feita atrav\u00E9s de um grafo direcionado ac\u00EDclico no qual os n\u00F3s representam vari\u00E1veis de um dom\u00EDnio e os arcos representam a depend\u00EAncia condicional ou informativa entre as vari\u00E1veis. Para representar a for\u00E7a da depend\u00EAncia, s\u00E3o utilizadas probabilidades, associadas a cada grupo de n\u00F3s pais-filhos na rede (PEARL, 1988)."@pt , "A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Formally, Bayesian networks are directed acyclic graphs whose nodes represent variables, and whose missing edges encode conditional independencies between the variables. Nodes represent random variables, but in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node. If the parents are <math>m Boolean variables then the probability function could be represented by a table of entries, one entry for each of the possible combinations of its parents being true or false. Efficient algorithms exist that perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams."@en , "Bayesverkko on havainnollinen verkkokaavio, joka koostuu solmuista ja niiden v\u00E4lisist\u00E4 yhdyskaarista. Bayesverkko kuvaa satunnaismuuttujia ja niiden v\u00E4lisi\u00E4 ehdollisia todenn\u00E4k\u00F6isyyksi\u00E4. Bayesverkkoja k\u00E4ytet\u00E4\u00E4n esimerkiksi erilaisten koneiden ja laitteiden vikojen havaitsemiseen ja paikallistamiseen."@fi , "Les R\u00E9seaux bay\u00E9siens sont \u00E0 la fois : Des mod\u00E8les de repr\u00E9sentation des connaissances Des \"machines \u00E0 calculer\" les probabilit\u00E9s conditionnelles Pour un domaine donn\u00E9 (par exemple m\u00E9dical), on d\u00E9crit les relations causales entre variables d'int\u00E9r\u00EAt par un graphe. Dans ce graphe, les relations de cause \u00E0 effet entre les variables ne sont pas d\u00E9terministes, mais probabilis\u00E9es. Ainsi, l'observation d'une cause ou de plusieurs causes n'entra\u00EEne pas syst\u00E9matiquement l'effet ou les effets qui en d\u00E9pendent, mais modifie seulement la probabilit\u00E9 de les observer. L'int\u00E9r\u00EAt particulier des r\u00E9seaux bay\u00E9siens est de tenir compte simultan\u00E9ment de connaissances a priori d'experts (dans le graphe) et de l'exp\u00E9rience contenue dans les donn\u00E9es. Les r\u00E9seaux bay\u00E9siens sont surtout utilis\u00E9s pour le diagnostic, l'analyse de risques, la d\u00E9tection des spams et le data mining."@fr , "Una rete bayesiana (BN - Bayesian Network) \u00E8 un modello grafico probalistico."@it , "Een probabilistische netwerk is een datastructuur die gebruikt wordt om probabilistische redeneringen (of abstracter gezien kansverdelingen) te modelleren. Het is een gerichte acyclische graaf waarin de knopen (vertices) proposities/gebeurtenissen beschrijven, en de kanten (arcs) de relaties ertussen. Meestal wordt de richting van de kant gezien als een (bijna) oorzakelijk verband tussen de verschillende gebeurtenissen, dit is echter niet een noodzakelijkheid. Wel is de oorzakelijke interpretatie vaak een goede intu\u00EFtieve manier om (voor het eerst) een probabilistisch netwerk te lezen. Bij de knopen horen inschattingsfuncties. De wortel(s) van de graaf hebben inschattingsfuncties die vertellen hoe vaak ze optreden. Knopen met ouders hebben inschattingsfuncties die vertellen wat de kans is dat de bijbehorende gebeurtenissen optreden, gegeven de verschillende mogelijke configuraties van hun ouders."@nl , "Bayes'sche Netze dienen der Repr\u00E4sentation von nicht beobachtbaren Ereignissen und daraus m\u00F6glichen Schlussfolgerungen. Sie stellen eine spezielle Form der Formulierung von wahrscheinlichkeitstheoretischen Modellen dar. Ein Bayes'sches Netz ist ein gerichteter azyklischer Graph (DAG), in dem die Knoten Zufallsvariablen und die Kanten bedingte Abh\u00E4ngigkeiten zwischen den Variablen beschreiben. Jedem Knoten des Netzes ist eine bedingte Wahrscheinlichkeitsverteilung der durch ihn repr\u00E4sentierten Zufallsvariable gegeben, die Zufallsvariablen an den Elternknoten zuordnet. Sie werden durch Wahrscheinlichkeitstabellen beschrieben. Diese Verteilung kann beliebig sein, jedoch wird h\u00E4ufig mit diskreten oder Normalverteilungen gearbeitet. Eltern eines Knotens v sind diejenigen Knoten, von denen eine Kante zu v f\u00FChrt. Ein Bayes'sches Netz dient dazu, die gemeinsame Wahrscheinlichkeitsverteilung aller beteiligten Variablen unter Ausnutzung bekannter bedingter Unabh\u00E4ngigkeiten m\u00F6glichst kompakt zu repr\u00E4sentieren. Dabei wird die bedingte (Un)Abh\u00E4ngigkeit von Untermengen der Variablen mit dem a-priori Wissen kombiniert. Sind X1, ... , Xn einige der im Graphen vorkommenden Zufallsvariablen (die abgeschlossen sind unter hinzuf\u00FCgen von Elternvariablen), so berechnet sich deren gemeinsame Verteilung als <math>P(X_1,\\dots,X_n) = \\prod_{i=1}^{n}P(X_i | \\mathrm{parents})</math> Hat ein Knoten keine Eltern, so handelt es sich bei der assoziierten Wahrscheinlichkeitsverteilung um eine unbedingte Verteilung."@de ; rdfs:comment ""@ja , "Een probabilistische netwerk is een datastructuur die gebruikt wordt om probabilistische redeneringen (of abstracter gezien kansverdelingen) te modelleren. Het is een gerichte acyclische graaf waarin de knopen (vertices) proposities/gebeurtenissen beschrijven, en de kanten (arcs) de relaties ertussen. Meestal wordt de richting van de kant gezien als een (bijna) oorzakelijk verband tussen de verschillende gebeurtenissen, dit is echter niet een noodzakelijkheid."@nl , "Modelele grafice probabilistice sunt grafuri \u00EEn care nodurile reprezint\u0103 variabile aleatoare, iar arcele (respectiv lipsa acestora) reprezint\u0103 presupuneri de independen\u0163\u0103 condi\u0163ionat\u0103. Ca urmare, ele ofer\u0103 o reprezentare compact\u0103 a distribu\u0163iilor probabilistice cumulate."@ro , "Bayes'sche Netze dienen der Repr\u00E4sentation von nicht beobachtbaren Ereignissen und daraus m\u00F6glichen Schlussfolgerungen. Sie stellen eine spezielle Form der Formulierung von wahrscheinlichkeitstheoretischen Modellen dar. Ein Bayes'sches Netz ist ein gerichteter azyklischer Graph (DAG), in dem die Knoten Zufallsvariablen und die Kanten bedingte Abh\u00E4ngigkeiten zwischen den Variablen beschreiben."@de , "Les R\u00E9seaux bay\u00E9siens sont \u00E0 la fois : Des mod\u00E8les de repr\u00E9sentation des connaissances Des \"machines \u00E0 calculer\" les probabilit\u00E9s conditionnelles Pour un domaine donn\u00E9 (par exemple m\u00E9dical), on d\u00E9crit les relations causales entre variables d'int\u00E9r\u00EAt par un graphe. Dans ce graphe, les relations de cause \u00E0 effet entre les variables ne sont pas d\u00E9terministes, mais probabilis\u00E9es."@fr , "Una rete bayesiana (BN - Bayesian Network) \u00E8 un modello grafico probalistico."@it , "As Redes Bayesianas foram desenvolvidas in\u00EDcio dos anos 80 para facilitar a tarefa de predi\u00E7\u00E3o e \u201Cabdu\u00E7\u00E3o\u201D em sistemas de Intelig\u00EAncia Artificial (AI) (Pearl, 2000)."@pt , "\u0411\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0430\u044F \u0441\u0435\u0442\u044C (\u0438\u043B\u0438 \u0411\u0430\u0439\u0435\u0441\u043E\u0432\u0441\u043A\u0430\u044F \u0441\u0435\u0442\u044C \u0434\u043E\u0432\u0435\u0440\u0438\u044F) \u2014 \u044D\u0442\u043E \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u0430\u044F \u043C\u043E\u0434\u0435\u043B\u044C, \u043F\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043B\u044F\u044E\u0449\u0430\u044F \u0441\u043E\u0431\u043E\u0439 \u043C\u043D\u043E\u0436\u0435\u0441\u0442\u0432\u043E \u043F\u0435\u0440\u0435\u043C\u0435\u043D\u043D\u044B\u0445 \u0438 \u0438\u0445 \u0432\u0435\u0440\u043E\u044F\u0442\u043D\u043E\u0441\u0442\u043D\u044B\u0445 \u0437\u0430\u0432\u0438\u0441\u0438\u043C\u043E\u0441\u0442\u0435\u0439."@ru , "Una red bayesiana, o red de creencia, es un modelo probabil\u00EDstico multivariado que relaciona un conjunto de variables aleatorias mediante un grafo dirigido que indica expl\u00EDcitamente influencia causal. Gracias a su motor de actualizaci\u00F3n de probabilidades, el Teorema de Bayes, las redes bayesianas son una herramienta extremadamente \u00FAtil en la estimaci\u00F3n de probabilidades ante nuevas evidencias. Una red bayesiana es un tipo de red causal."@es , "Bayesverkko on havainnollinen verkkokaavio, joka koostuu solmuista ja niiden v\u00E4lisist\u00E4 yhdyskaarista. Bayesverkko kuvaa satunnaismuuttujia ja niiden v\u00E4lisi\u00E4 ehdollisia todenn\u00E4k\u00F6isyyksi\u00E4. Bayesverkkoja k\u00E4ytet\u00E4\u00E4n esimerkiksi erilaisten koneiden ja laitteiden vikojen havaitsemiseen ja paikallistamiseen."@fi , "A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases."@en , "Sie\u0107 bayesowska s\u0142u\u017Cy do przedstawiania zale\u017Cno\u015Bci pomi\u0119dzy zdarzeniami bazuj\u0105c na rachunku prawdopodobie\u0144stwa. Klasycznym przyk\u0142adem jest reprezentowanie zale\u017Cno\u015Bci pomi\u0119dzy symptomami a chorob\u0105. Formalnie taka sie\u0107 jest modelowana za pomoc\u0105 skierowanego grafu acyklicznego, w kt\u00F3rym wierzcho\u0142ki reprezentuj\u0105 zdarzenia, a \u0142uki zwi\u0105zki przyczynowe pomi\u0119dzy tymi zdarzeniami. Je\u015Bli od wierzcho\u0142ka A prowadzi \u015Bcie\u017Cka do wierzcho\u0142ka B to B jest potomkiem A."@pl . @prefix skos: . @prefix ns12: . dbpedia:Bayesian_network skos:subject ns12:Statistical_models , ns12:Bayesian_statistics , ns12:Networks , ns12:Bayesian_networks , ns12:Graphical_models . @prefix ns13: . dbpedia:Bayesian_network dbpprop:wikiPageUsesTemplate ns13:columns-list , ns13:portalbox , ns13:article_issues , ns13:rp ; dbpprop:date "May 2009"@en ; dbpprop:restructure "December 2008"@en . @prefix dbpedia-owl: . dbpedia:Bayesian_network dbpprop:width "95"^^dbpedia-owl:Percent ; dbpprop:image "Fisher iris versicolor sepalwidth.svg"@en , "ArtificialFictionBrain.png"@en ; dbpprop:citationsMissing "May 2009"@en . @prefix ns15: . dbpedia:Bayesian_network dbpprop:relatedInstance ns15:sfn4 , ns15:sfn1 , ns15:sfn2 , ns15:sfn3 ; dbpprop:margin "0px"@en . @prefix ns16: . dbpedia:Bayesian_network dbpprop:hasPhotoCollection ns16:Bayesian_network . dbpedia:Bayesian dbpprop:disambiguates dbpedia:Bayesian_network . dbpedia:Bayes_net dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_belief_net dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_belief_network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Belief_network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayes_networks dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_Networks dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_net dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_networks dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Baysian_network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Learning_bayesian_network_structure dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_neural_network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Belief_networks dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Inference_network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_Network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayes_network dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_Graphical_Model dbpprop:redirect dbpedia:Bayesian_network . dbpedia:Bayesian_graphical_model dbpprop:redirect dbpedia:Bayesian_network . @prefix yago: . yago:Bayesian_network owl:sameAs dbpedia:Bayesian_network .