標(biāo)題: Titlebook: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis; Uffe B. Kj?rulff,Anders L. Madsen Book 2013Latest edition [打印本頁(yè)] 作者: 變成小松鼠 時(shí)間: 2025-3-21 17:33
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書目名稱Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis讀者反饋學(xué)科排名
作者: Mercurial 時(shí)間: 2025-3-21 20:42
Networks of a graph and the interactions (direct dependences) as directed edges (links or arcs) between the vertices. Any pair of unconnected vertices of such a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be re作者: 6Applepolish 時(shí)間: 2025-3-22 02:01
Probabilitiesrsive factorization of a joint probability distribution into a product of lower-dimensional conditional probability distributions. First, any joint probability distribution can be decomposed (or factorized) into a product of conditional distributions of different dimensionality, where the dimensiona作者: 值得尊敬 時(shí)間: 2025-3-22 07:41 作者: Melatonin 時(shí)間: 2025-3-22 11:02 作者: 似少年 時(shí)間: 2025-3-22 13:55
Eliciting the Model]. The structure of a probabilistic network is often referred to as the qualitative part of the network, whereas the parameters are often referred to as its quantitative part. As the parameters of a model are determined by its structure, the model elicitation process always proceeds in two consecuti作者: DOSE 時(shí)間: 2025-3-22 20:21 作者: 安定 時(shí)間: 2025-3-22 23:33
Data-Driven Modelingases and expert knowledge consists of two main steps. The first step is to induce the structure of the model, that is, the DAG, while the second step is to estimate the parameters of the model as defined by the structure. In this chapter we consider only discrete Bayesian networks. Thus, the task of作者: 玉米棒子 時(shí)間: 2025-3-23 01:57 作者: 玩笑 時(shí)間: 2025-3-23 06:52 作者: FAWN 時(shí)間: 2025-3-23 13:02 作者: 時(shí)代 時(shí)間: 2025-3-23 15:12 作者: 大溝 時(shí)間: 2025-3-23 21:26
1613-9011 wledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. .978-1-4939-0029-9978-1-4614-5104-4Series ISSN 1613-9011 Series E-ISSN 2197-4128 作者: 殘忍 時(shí)間: 2025-3-24 02:10
Book 2013Latest editioneling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. .作者: crutch 時(shí)間: 2025-3-24 03:32
Book 2013Latest edition wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix.? Intended primarily for practitioners, this book does not 作者: 誘惑 時(shí)間: 2025-3-24 09:10
https://doi.org/10.1007/978-3-319-14874-8tements of local conditional independences manifest themselves as reductions of dimensionalities of some of the conditional probability distributions. Most often, these independence statements give rise to dramatic reductions of complexity of the DAG such that the resulting DAG appears to be quite sparse.作者: 無(wú)能力 時(shí)間: 2025-3-24 11:00 作者: figure 時(shí)間: 2025-3-24 18:05
Probabilitiestements of local conditional independences manifest themselves as reductions of dimensionalities of some of the conditional probability distributions. Most often, these independence statements give rise to dramatic reductions of complexity of the DAG such that the resulting DAG appears to be quite sparse.作者: cardiopulmonary 時(shí)間: 2025-3-24 22:18
Eliciting the Modelrt of the model. Second, once the model structure has been determined through an iterative process involving testing of variables and conditional independences, and verification of the directionality of the links, the values of the parameters are elicited.作者: 浮雕寶石 時(shí)間: 2025-3-25 02:33 作者: 影響帶來 時(shí)間: 2025-3-25 06:36 作者: aristocracy 時(shí)間: 2025-3-25 09:00
Revising Policy to Reflect Our Better Natureence need not be inconsistent with the model in order for the results to be unreliable. It may be that evidence is simply in conflict with the model. This implies that the model in relation to the evidence may be weak, and therefore the results may be unreliable.作者: 平息 時(shí)間: 2025-3-25 14:12 作者: lacrimal-gland 時(shí)間: 2025-3-25 16:16 作者: Meander 時(shí)間: 2025-3-25 21:17
Developments in Obstetrics and Gynecologya process of deriving conclusions (new pieces of knowledge) by manipulating a (large) body of knowledge, typically including definitions of entities (objects, concepts, events, phenomena, etc.), relations among them, and observations of states (values) of some of the entities.作者: Intuitive 時(shí)間: 2025-3-26 03:56 作者: 虛假 時(shí)間: 2025-3-26 07:14
Development of the Human Fetal Brain a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.作者: 充滿人 時(shí)間: 2025-3-26 12:25 作者: aneurysm 時(shí)間: 2025-3-26 16:26
Networks a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.作者: Muffle 時(shí)間: 2025-3-26 17:44
Probabilistic Networks a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.作者: 合唱隊(duì) 時(shí)間: 2025-3-26 23:30 作者: concert 時(shí)間: 2025-3-27 03:36 作者: 拋射物 時(shí)間: 2025-3-27 08:50 作者: notice 時(shí)間: 2025-3-27 10:49 作者: VAN 時(shí)間: 2025-3-27 15:50
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis978-1-4614-5104-4Series ISSN 1613-9011 Series E-ISSN 2197-4128 作者: 詳細(xì)目錄 時(shí)間: 2025-3-27 18:36
Amar Nijagal M.D.,Tippi C. MacKenzie M.D.order, for instance, to simplify knowledge elicitation and model specification, capture certain properties of the problem domain that are not easily captured by an acyclic, directed graph, to reduce model complexity and improve efficiency of inference in the model, and so on.作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-3-27 22:35
Josep Maria Tarragona i Clarasó, the posterior probability of a single hypothesis variable is sometimes of interest. When the evidence set consists of a large number of findings or even when it consists of only a small number of findings, questions concerning the impact of subsets of the evidence on the hypothesis or a competing hypothesis emerge.作者: 后天習(xí)得 時(shí)間: 2025-3-28 05:35
Developments in Obstetrics and Gynecologybe programmed to execute an arbitrary set of manipulations on numbers and symbols. Solving an intellectually challenging task can be characterized as a process of deriving conclusions (new pieces of knowledge) by manipulating a (large) body of knowledge, typically including definitions of entities (作者: isotope 時(shí)間: 2025-3-28 08:55 作者: Genistein 時(shí)間: 2025-3-28 14:05
https://doi.org/10.1007/978-3-319-14874-8rsive factorization of a joint probability distribution into a product of lower-dimensional conditional probability distributions. First, any joint probability distribution can be decomposed (or factorized) into a product of conditional distributions of different dimensionality, where the dimensiona作者: Heart-Rate 時(shí)間: 2025-3-28 18:29 作者: Tidious 時(shí)間: 2025-3-28 22:31 作者: COMMA 時(shí)間: 2025-3-29 02:33 作者: 敏捷 時(shí)間: 2025-3-29 06:38 作者: Arroyo 時(shí)間: 2025-3-29 10:53 作者: peptic-ulcer 時(shí)間: 2025-3-29 11:56
Revising Policy to Reflect Our Better Naturean approximation of a problem domain that is designed to be applied according to the assumptions as determined by the background condition or context of the model. If a model is used under circumstances not consistent with the background condition, the results will in general be unreliable. The evid
