bayesian network を様々なネットショップで比較

Situation Assessment in Aviation Bayesian Network and Fuzzy Logic-based Approaches【電子書籍】[ Jitendra R. Raol ]

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価格￥35,471

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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 ...

Bayesian Decision Networks Fundamentals and Applications【電子書籍】[ Fouad Sabry ]

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価格￥600

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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...

Enhanced Bayesian Network Models for Spatial Time Series Prediction Recent Research Trend in Data-Driven Predictive Analytics【電子書籍】[ Monidipa Das ]

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価格￥18,231

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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...

Bayesian Networks and Decision Graphs (Information Science and Statistics) (English Edition)

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Bayesian Networks and Decision Graphs (Information Science and Statistics)

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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis【電子書籍】[ Uffe B. Kj?rulff ]

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価格￥12,154

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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...

Benefits of Bayesian Network Models【電子書籍】[ Philippe Weber ]

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価格￥21,847

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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 ...

Bayesian Network Modeling of Corrosion【電子書籍】

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価格￥21,878

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＜p＞This book represents a compilation of experience from a slate of experts involved in developing and deploying Bayesian Networks (BN) for corrosion management. The contributors describe how probability distributions can be developed for corroding systems and BN can be applied as an ideal framework to deal with corrosion risk. Corrosion can develop suddenly and grow rapidly after a long incubation period and take many non-uniform aspects, including pitting and stress corrosion cracking, that cannot be mitigated by simply bulking up the system. They also describe how complex engineering structures and systems are influenced by many natural and engineering factors that come together in myriad ways. It provides a broad perspective to the reader on the potential of BN as an artificial intelligence tool for corrosion risk management and the challenges for implementing it.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらな...

洋書 Springer paperback Book, Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (Information Science and Statistics)

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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...

Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)

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Bayesian Network Modeling Uncertainty in Robotics Systems【電子書籍】[ Fouad Sabry ]

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価格￥600

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＜p＞1: Bayesian network: Delve into the foundational concepts of Bayesian networks and their applications.＜/p＞＜p＞2: Statistical model: Explore the framework of statistical models crucial for data interpretation.＜/p＞＜p＞3: Likelihood function: Understand the significance of likelihood functions in probabilistic reasoning.＜/p＞＜p＞4: Bayesian inference: Learn how Bayesian inference enhances decisionmaking processes with data.＜/p＞＜p＞5: Pattern recognition: Investigate methods for recognizing patterns in complex data sets.＜/p＞＜p＞6: Sufficient statistic: Discover how sufficient statistics simplify data analysis while retaining information.＜/p＞＜p＞7: Gaussian process: Examine Gaussian processes and their role in modeling uncertainty.＜/p＞＜p＞8: Posterior probability: Gain insights into calculating posterior probabilities for informed predictions.＜/p＞＜p＞9: Graphical model: Understand the structure and utility of graphical models in representing relationsh...

洋書 Springer Paperback, Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings (Lecture Notes in Computer Science (9505))

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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...

Modeling and Reasoning with Bayesian Networks (English Edition)

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Learning Bayesian Networks

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Bayesian Network Fundamentals and Applications【電子書籍】[ Fouad Sabry ]

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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...

Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science) (English Edition)

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洋書 LAP Lambert Academic Publishing paperback Book, 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...

Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks Online Environmental Field Reconstruction in Space and Time【電子書籍】[ Yunfei Xu ]

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価格￥6,076

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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...

Bayesian Networks An Introduction【電子書籍】[ Timo Koski ]

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価格￥15,531

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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...

Bayesian Networks for Reliability Engineering【電子書籍】[ Baoping Cai ]

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価格￥18,231

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＜p＞This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。

Bayesian Networks in R: with Applications in Systems Biology (Use R! Book 48) (English Edition)

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Bayesian Networks With Examples in R【電子書籍】[ Marco Scutari ]

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価格￥20,218

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＜p＞＜strong＞Bayesian Networks: With Examples in R, Second Edition＜/strong＞ introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation.＜/p＞＜p＞The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigor...

Bayesian Networks in R with Applications in Systems Biology【電子書籍】[ Radhakrishnan Nagarajan ]

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価格￥9,723

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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 ...

Test Data Engineering Latent Rank Analysis, Biclustering, and Bayesian Network【電子書籍】[ Kojiro Shojima ]

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価格￥17,016

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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...

洋書 Springer paperback Book, Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (Information Science and Statistics, 22)

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価格￥23,602

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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...

Bayesian Networks: A Practical Guide to Applications (Statistics in Practice)

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洋書 Springer paperback Book, The Manual of Strategic Economic Decision Making: Using Bayesian Belief Networks to Solve Complex Problems

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価格￥14,485

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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...

R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

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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

Risk Assessment and Decision Analysis with Bayesian Networks (English Edition)

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Causal Inference with Bayesian Networks: Exploring the Practical Applications and Demonstrations of Causal Inference using R and Python (English Edition)

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bayesian network