bayesian network

洋書 Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis: A Guide to Construction and Analysis (Information Science and Statistics)Risk Assessment and Decision Analysis with Bayesian Networks【電子書籍】 Norman FentonBayesian Networks and Influence Diagrams: A Guide to Construction and Analysis【電子書籍】 Uffe B. Kj rulffBayesian Networks An Introduction【電子書籍】 Timo KoskiSituation Assessment in Aviation Bayesian Network and Fuzzy Logic-based Approaches【電子書籍】 Jitendra R. RaolBayesian Decision Networks Fundamentals and Applications【電子書籍】 Fouad Sabry洋書 Paperback, Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings (Lecture Notes in Computer Science (9505))Enhanced Bayesian Network Models for Spatial Time Series Prediction Recent Research Trend in Data-Driven Predictive Analytics【電子書籍】 Monidipa DasLearning Bayesian NetworksRと事例で学ぶベイジアンネットワーク / 原タイトル:Bayesian Networks 原著第2版の翻訳 本/雑誌 / MarcoScutari/著 Jean‐BaptisteDenis/著 金明哲/監訳 財津亘/訳【中古】【輸入品 未使用】Bayesian Networks: With Examples in R (Chapman Hall/CRC Texts in Statistical Science)Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks Online Environmental Field Reconstruction in Space and Time【電子書籍】 Yunfei Xu洋書 Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (Information Science and Statistics)Bayesian Networks in Educational Assessment【電子書籍】 Russell G. AlmondBayesian Networks in R with Applications in Systems Biology【電子書籍】 Radhakrishnan NagarajanBayesian Network Fundamentals and Applications【電子書籍】 Fouad SabryBenefits of Bayesian Network Models【電子書籍】 Philippe Weber洋書 The Manual of Strategic Economic Decision Making: Using Bayesian Belief Networks to Solve Complex ProblemsTest Data Engineering Latent Rank Analysis, Biclustering, and Bayesian Network【電子書籍】 Kojiro Shojima洋書 Hosny Manar Paperback, An Adaptive Hybrid Genetic Algorithm Simulated Annealing Approach: A New Hybridization Technique Applied to Solving the MAP Problem in Bayesian Belief Networks
 

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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.</p> <p><strong>Features</strong></p> <ul> <li>Provides all tools necessary to build and run realistic Bayesian network models</li> <li></li> <li>Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cyberse...
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  • <p><em>Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition,</em> provides a comprehensive guide for practitioners who 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 require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models hav...
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  • <p><em>Bayesian Networks: An Introduction</em> provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout.</p> <p>Features include:</p> <ul> <li>An introduction to Dirichlet Distribution, Exponential Families and their applications.</li> <li>A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.</li> <li>A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning.</li> <li>All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.</li> </ul...
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  • <p><em>Situation Assessment in Aviation</em> focuses on new aspects of soft computing technologies for the evaluation and assessment of situations in aviation scenarios. It considers technologies emerging from multisensory data fusion (MSDF), Bayesian networks (BN), and fuzzy logic (FL) to assist pilots in their decision-making.</p> <p>Studying MSDF, BN, and FL from the perspective of their applications to the problem of situation assessment, the book discusses the development of certain soft technologies that can be further used for devising more sophisticated technologies for a pilot's decision-making when performing certain tasks: airplane monitoring, pair formation, attack, and threat. It explains the concepts of situation awareness, data fusion, decision fusion, Bayesian networks, fuzzy logic type 1, and interval type 2 fuzzy logic. The book also presents a hybrid technique by using BN and FL and a unique approach to the problem of situation assessment, beyond visual ...
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  • <p><strong>What Is Bayesian Decision Networks</strong></p> <p>A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional relationships via a directed acyclic graph (DAG). In other words, a Bayesian network is a type of directed acyclic graph. Bayesian networks are perfect for determining the likelihood that any one of multiple possible known causes was the contributing factor in an event that has already taken place and making a prediction based on that likelihood. For instance, the probabilistic links that exist between diseases and symptoms might be represented by a Bayesian network. The network may be used to compute the odds of the presence of a variety of diseases based on the symptoms that are provided.</p> <p><strong>How You Will Benefit</strong></p> <p>(I) Insights, and validations about the following topics:</p> <p>Chapter 1: Bayesian network</p> <p>Chapter 2: Influence diagram</p> <p>Chapter...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.</p> <p>The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented thr...
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  • 【30日間返品保証】商品説明に誤りがある場合は、無条件で弊社送料負担で商品到着後30日間返品を承ります。ご満足のいく取引となるよう精一杯対応させていただきます。※下記に商品説明およびコンディション詳細、出荷予定・配送方法・お届けまでの期間について記載しています。ご確認の上ご購入ください。【インボイス制度対応済み】当社ではインボイス制度に対応した適格請求書発行事業者番号(通称:T番号・登録番号)を印字した納品書(明細書)を商品に同梱してお送りしております。こちらをご利用いただくことで、税務申告時や確定申告時に消費税額控除を受けることが可能になります。また、適格請求書発行事業者番号の入った領収書・請求書をご注文履歴からダウンロードして頂くこともできます(宛名はご希望のものを入力して頂けます)。■商品名■Learning Bayesian Networks■出版社■Prentice Hall■著者■Neapolitan Richard E.■発行年■2003/04/01■ISBN10■0130125342■ISBN13■9780130125347■コンディションランク■可コンディションランク説明ほぼ新品:未使用に近い状態の商品非常に良い:傷や汚れが少なくきれいな状態の商品良い:多少の傷や汚れがあるが、概ね良好な...
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  • ご注文前に必ずご確認ください<商品説明><収録内容>1 離散型データ事例:多項ベイジアンネットワーク2 連続型データ事例:ガウシアン・ベイジアンネットワーク3 混合(離散・連続型)事例:条件付きガウシアン・ベイジアンネットワーク4 時系列データ:ダイナミック・ベイジアンネットワーク5 より複雑な事例:汎用ベイジアンネットワーク6 ベイジアンネットワークの理論とアルゴリズム7 ベイジアンネットワークのためのソフトウェア8 実社会におけるベイジアンネットワークの応用付録A グラフ理論付録B 確率分布付録C ベイジアンネットワークの覚書き<商品詳細>商品番号:NEOBK-2725163MarcoScutari / Cho Jean BaptisteDenis / Cho Kin Meitetsu / Kanyaku Zaitsu Wataru / Yaku / R to Jirei De Manabu Bayesian Network / Hara Title : Bayesian Networks Gencho Dai2 Han No Honyakuメディア:本/雑誌重量:580g発売日:2022/04JAN:9784320114654Rと事例で学ぶベイジアンネットワーク / 原タイトル:Bayesian Networks 原著第2版の翻訳[本/雑誌] / MarcoScutari/著 Jean‐BaptisteDenis/著 金明哲/監訳 財津亘/訳2022/04発売
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  • 【中古】【輸入品・未使用】Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)【メーカー名】Chapman and Hall/CRC【メーカー型番】【ブランド名】Chapman and Hall/CRC【商品説明】Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)当店では初期不良に限り、商品到着から7日間は返品を 受付けております。こちらは海外販売用に買取り致しました未使用品です。買取り致しました為、中古扱いとしております。他モールとの併売品の為、完売の際はご連絡致しますのでご了承下さい。速やかにご返金させて頂きます。ご注文からお届けまで1、ご注文⇒ご注文は24時間受け付けております。2、注文確認⇒ご注文後、当店から注文確認メールを送信します。3、配送⇒当店海外倉庫から取り寄せの場合は10〜30日程度でのお届けとなります。国内到着後、発送の際に通知にてご連絡致します。国内倉庫からの場合は3〜7日でのお届けとなります。 ※離島、北海道、九州、沖縄は遅れる場合がございます。予めご了承下さい。お電話でのお問合せは少人数で運営の為受け付けておりませんので、メールにてお問合せお...
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  • <p>This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments.</p> <p>Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III descr...
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  • <p><strong>Bayesian Networks in R with Applications in Systems Biology</strong> is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel ...
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  • <p><strong>What Is Bayesian Network</strong></p> <p>A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional relationships via a directed acyclic graph (DAG). In other words, a Bayesian network is a type of directed acyclic graph. Bayesian networks are perfect for determining the likelihood that any one of multiple possible known causes was the contributing factor in an event that has already taken place and making a prediction based on that likelihood. For instance, the probabilistic links that exist between diseases and symptoms might be represented by a Bayesian network. The network may be used to compute the odds of the presence of a variety of diseases based on the symptoms that are provided.</p> <p><strong>How You Will Benefit</strong></p> <p>(I) Insights, and validations about the following topics:</p> <p>Chapter 1: Bayesian Network</p> <p>Chapter 2: Likelihood Function</p> <p>Chapter 3: Baye...
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  • <p>The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field.</p> <p>Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty.</p> <p>This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems.</p> <p>Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real ...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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  • <p>This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the structure within the data to be visualized, and thereby make test results useful for students, teachers, and the society. The author does not differentiate test data analysis from data engineering and information visualization. This monograph introduces the following methods of engineering or processing test data, including the latest machine learning techniques: classical test theory (CTT), item response theory (IRT), latent class analysis (LCA), latent rank analysis (LRA), biclustering (co-clustering), and Bayesian network model (BNM). CTT and IRT are methods for analyzing test data and evaluating students’ abilities on a continuous scale. LCA and LRA assess examinees by classifying them into nominal and ordinal clusters, respectively, where the adequate number of clusters is estimated from the data. Biclustering classifies examinees into group...
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  • *** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 ・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。 ・お名前またはご住所が法人・団体名義(XX株式会社等)、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。 ・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。 ・当店でご購入された商品は、原則として、「個...
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