Titlebook: Reasoning with Probabilistic and Deterministic Graphical Models; Exact Algorithms Rina Dechter Book 2013 Springer Nature Switzerland AG 201

Capricious

書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models影響因子(影響力)




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models被引頻次




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models被引頻次學(xué)科排名




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models年度引用




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models年度引用學(xué)科排名




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models讀者反饋




書(shū)目名稱(chēng)Reasoning with Probabilistic and Deterministic Graphical Models讀者反饋學(xué)科排名

神圣不可

暖昧關(guān)系

Cpap155

Inference: Bucket Elimination for Probabilistic Networks,or the three primary queries defined over probabilistic networks: (1) belief-updating or computing posterior marginals (bel) as well as finding the probability of evidence; (2) finding the most probable explanation (mpe); and (3) finding the maximum a posteriori hypothesis (map).

Accommodation

跳脫衣舞的人

AND/OR Search Spaces and Algorithms for Graphical Models,value to a variable. To recall, algorithms for processing graphical models fall into two general types: inference-based and search-based. Inference-based algorithms (., variable-elimination, tree-clustering discussed earlier) are good at exploiting the independencies displayed by the underlying grap

In-Situ

Combining Search and Inference: Trading Space for Time, over an AND/OR space and if, in addition, we use context-based caching, search has the same worst-case complexity as inference, both are time and space exponentially bounded by the treewidth. However, when the treewidth is too high, the needed memory is not available and schemes that are more flexi

ONYM

Conclusion,able to general graphical models, whose functions can come from a variety of frameworks and applications (constraints, Boolean, probabilities, costs etc.). These include, constraint networks and SAT models, Bayesian networks, Markov random fields, Cost networks, and Influence diagrams. Therefore, th

遍及

DALLY

Conclusion,., but we also showed the relevance of related features such as . of pseudo trees, . and . We showed that both inference and search scheme are bounded exponentially by any of these parameters, and some combination of those hint at how we can trade memory for time.