作者: Individual 時間: 2025-3-21 23:41 作者: 失望昨天 時間: 2025-3-22 02:18
Graph Theoryition of directed and undirected graphs, some basic theoretical graph concepts are introduced, including types of graphs, trajectories and circuits, and graph isomorphism. A section is dedicated to trees, an important type of graph. Some more advanced theoretical graph aspects required for inference作者: NIB 時間: 2025-3-22 06:52
Bayesian Classifiersl as its main variants: TAN and BAN. Then the semi-naive Bayesian classifier is described. A multidimensional classifier may assign several classes to the same object. Two alternatives for multidimensional classification are analyzed: the multidimensional Bayesian network classifier and the Bayesian作者: sorbitol 時間: 2025-3-22 12:46
Hidden Markov Modelsns, this chapter focuses on hidden Markov models. The algorithms for solving the basic problems: . and . are presented. Next a description of Gaussian HMMs and several extensions to the basic HMM are given. The chapter concludes with two applications: the “PageRank” procedure used by Google and gest作者: 圖畫文字 時間: 2025-3-22 15:39
Markov Random Fieldse how a Markov random field is represented, including its structure and parameters, with emphasis on regular MRFs. Then, a general stochastic simulation algorithm to find the . configuration of a MRF is described, including some of its main variants. The problem of parameter estimation for a MRF is 作者: 神秘 時間: 2025-3-22 17:48
Bayesian Networks: Representation and Inferenceed, including the concept of D-Separation and the independence axioms. With respect to parameter specification, the two main alternatives for a compact representation are described, one based on canonical models and the other on graphical representations. Then the algorithms for probabilistic infere作者: 漫不經(jīng)心 時間: 2025-3-22 23:22
Bayesian Networks: Learninghandle uncertainty in the parameters and missing data; it also includes the basic discretization techniques. After describing the techniques for learning tree and polytree BNs, the two main types of methods for structure learning are described: score and search, and independence tests. We then descr作者: PAC 時間: 2025-3-23 01:49 作者: stratum-corneum 時間: 2025-3-23 06:47
Decision Graphsrees and their evaluation strategy. Thirdly, influence diagrams are introduced, including three alternative evaluation strategies: transformation to a decision tree, variable elimination and transformation to a Bayesian network. The chapter concludes with two application examples: a decision support作者: profligate 時間: 2025-3-23 11:16 作者: 殺死 時間: 2025-3-23 14:14 作者: 滔滔不絕的人 時間: 2025-3-23 20:06 作者: 怒目而視 時間: 2025-3-23 22:29
Graphical Causal Modelsortance. Then causal Bayesian networks are described, including two types of causal reasoning, prediction and counterfactuals. It introduces the . and . criteria, to take into account covariates that can affect causal inference. The chapter concludes with two examples of applications of causal model作者: HEED 時間: 2025-3-24 06:15
Causal Discovery Then the types of graphs used to represent partial models are introduced. Several algorithms for causal discovery from observational data are explained, including score-based and constraint-based; as well as a technique for linear models. Finally, it includes an example of learning causal models fr作者: Cocker 時間: 2025-3-24 08:10
Luis Enrique Sucarementary and interrelated perspectives, at economy and firm-This book presents peer-reviewed, state-of-the-art conceptual and empirical papers devoted to changes in the international competitive position of the Central and Eastern European (CEE) region, its countries and businesses.?While the unprec作者: 刻苦讀書 時間: 2025-3-24 12:21 作者: entitle 時間: 2025-3-24 16:15 作者: ANTI 時間: 2025-3-24 22:27
Luis Enrique Sucarand food security patterns over 25 years.Includes nine policThe book combines food security and agricultural competitiveness issues and treat them together. It starts with definitions and evolution of both concepts, followed by reviews on global and regional food security challenges. The book identi作者: Fibroid 時間: 2025-3-25 02:44
Luis Enrique Sucarade patterns and trends at various levels. We then, devise five categories of countries according to their net agricultural and food trade position and income level. The chapter ends with an analysis of reasons behind recent changes in global trade of agricultural products. The chapter suggests that作者: 鋼筆記下懲罰 時間: 2025-3-25 06:16
Luis Enrique Sucars and services. A company’s competitiveness on both price and quality is inevitably related to innovation. The problem area of competitiveness has gradually broadened from the corporate level and thus innovations have become an inevitable aspect of national economies and have achieved rising importa作者: 濕潤 時間: 2025-3-25 09:48 作者: 碌碌之人 時間: 2025-3-25 12:09 作者: 脊椎動物 時間: 2025-3-25 19:18 作者: 按等級 時間: 2025-3-25 19:59
Textbook 2021Latest editionrom an engineering perspective.? It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python..Th作者: Fibroid 時間: 2025-3-26 01:48 作者: 機(jī)警 時間: 2025-3-26 07:55
Bayesian Networks: Learninging tree and polytree BNs, the two main types of methods for structure learning are described: score and search, and independence tests. We then describe how to combine expert knowledge and data. The chapter concludes with two application examples, one in the area of pollution modeling and other for agricultural planning.作者: osteopath 時間: 2025-3-26 12:25
Decision Graphs decision tree, variable elimination and transformation to a Bayesian network. The chapter concludes with two application examples: a decision support system for lung cancer and a decision model that acts as a caregiver to guide an elderly or handicapped person in cleaning her hands.作者: nocturia 時間: 2025-3-26 13:48
Markov Decision Processestion based on graphical models to solve very large MDPs. Abstraction and Decomposition of MDPs are introduced, as alternatives to simplify large problems. The chapter concludes by describing two applications of MDPs: power plant control and service robot task coordination.作者: 運(yùn)動的我 時間: 2025-3-26 17:25
Partially Observable Markov Decision Processes equations (.) is presented via a series of examples, which is the bases of several solution algorithms. The value iteration and point-based value iteration algorithms for finding the optimal policy are described. Finally, two application examples are presented: automatic adaption for virtual rehabilitation and task planning for service robots.作者: Eeg332 時間: 2025-3-26 23:09 作者: Cosmopolitan 時間: 2025-3-27 03:01
Causal Discoveryed, including score-based and constraint-based; as well as a technique for linear models. Finally, it includes an example of learning causal models from real world data about children with attention deficit hyperactivity disorder; and an application for discovering brain effective connectivity networks based on neuro-images.作者: 起皺紋 時間: 2025-3-27 06:48 作者: NUL 時間: 2025-3-27 09:39
Bayesian Classifiers chain classifier. Then an introduction to hierarchical classification is presented. The chapter concludes by illustrating the application of Bayesian classifiers in two domains: skin pixel detection in images and drug selection for HIV treatment.作者: 燈絲 時間: 2025-3-27 16:15
Markov Random Fieldsaddressed, considering the maximum likelihood estimator. Conditional random fields are also introduced. The chapter concludes with two applications of MRFs for image analysis, one for image de-noising, and the other for improving image annotation by including spatial relations.作者: propose 時間: 2025-3-27 18:05
Bayesian Networks: Representation and Inferencence are introduced, including belief propagation, variable elimination, conditioning, junction trees, loopy propagation and stochastic simulation. The chapter concludes by illustrating the application of Bayesian networks in information validation and system reliability analysis.
