Bayesian Networks (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition
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Bayesian Networks (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach.
Bayesian Networks (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition
Numéro d'article: 38684347

Bayesian Networks (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition

Numéro d'article: 38684347

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Get the 2nd edition of Bayesian Networks Chapman & HallCRC Texts in Statistical Science at Ubuy Switzerland. Explore a wide range of statistical science books online.
  • Hands-on approach to introduce Bayesian networks with examples in R
  • Significant new material on modern machine-learning practice included
  • Covers structure learning, parameter learning, and inference
  • Explores dynamic networks, networks with heterogeneous variables, and model validation
  • Includes introduction to causal Bayesian networks and overview of R packages implementing Bayesian networks
  • Online supplementary materials available on https://www.bnlearn.com/book-crc-2ed/
Item Weight2.5 lbs (1.13 kg)

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Bayesian Networks (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition

About This Item

Introducing the 2nd Edition of "Bayesian Networks Chapman & HallCRC Texts in Statistical Science" - the ultimate guide for mastering Bayesian networks and statistical science. Whether you're a student, researcher, or professional, this textbook is packed with comprehensive knowledge and practical examples to enhance your understanding and application of Bayesian networks. In this extensively revised edition, you'll delve into the fascinating world of Bayesian networks, exploring their foundations, principles, and advanced techniques. Gain a solid foundation in statistical science and learn how to effectively analyze complex data sets using Bayesian methods. With its clear and concise explanations, this book is perfect for both beginners and experienced practitioners.

Each chapter is thoughtfully structured, presenting concepts in a logical sequence and building upon previous knowledge. You'll find numerous real-world examples, case studies, and exercises throughout the book, allowing you to apply what you've learned and strengthen your skills. The significance of Bayesian networks in various fields cannot be overstated. This textbook specifically caters to individuals interested in statistical science as well as those working in areas such as machine learning, data science, artificial intelligence, and decision making.

It is an indispensable resource for anyone seeking to unravel complex relationships, make predictions, and gain insights from data. Chapman & HallCRC, a renowned publisher of scientific texts, has ensured the highest quality of content and accuracy in this 2nd edition. The authors, experts in their respective fields, bring their extensive knowledge and experience to deliver a comprehensive and up-to-date resource. Invest in your professional growth and expand your expertise by adding "Bayesian Networks Chapman & HallCRC Texts in Statistical Science 2nd Edition" to your library. Order now and embark on a journey of discovery and mastery in the world of Bayesian networks and statistical science.

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Questions et réponses des clients

  • question: What are Bayesian networks and how are they used in statistics?

    répondre: Bayesian networks are graphical models that represent the probabilistic relationships among a set of variables. They provide a way of encapsulating knowledge about uncertainty and make it easier to compute various probabilities. These networks are commonly used in areas such as machine learning, natural language processing, and bioinformatics, enabling researchers and practitioners to build models that predict outcomes based on specific inputs. For example, in medical diagnostics, Bayesian networks can help in determining the likelihood of a disease given certain symptoms, thereby aiding healthcare professionals in decision-making.
  • question: What is the significance of the second edition of Bayesian Networks by Chapman & Hall?

    répondre: The second edition of Bayesian Networks published by Chapman & Hall offers updated insights, methodologies, and relevant case studies that reflect advancements in the field since the first edition. It incorporates new research findings and applications that enhance the understanding of Bayesian methodologies. This is particularly important for students and professionals who need to stay current with best practices in statistical science. For instance, it provides clearer examples and practical scenarios that illustrate the application of Bayesian networks in real-world situations.
  • question: Who is the target audience for Bayesian Networks, Chapman & Hall/CRC Texts in Statistical Science?

    répondre: The target audience includes statisticians, data scientists, students studying probability and statistics, and professionals in various fields such as artificial intelligence and data analytics. It's suitable for both academic researchers and practitioners who seek a robust understanding of Bayesian methods. The book serves as an educational resource while also offering practical applications. For example, a graduate student in statistics may use this text to deepen their understanding of Bayesian inference, while a data analyst may apply the concepts in predictive modeling in their work.
  • question: What topics are covered in the Bayesian Networks 2nd edition?

    répondre: The second edition covers a range of topics including the foundations of Bayesian probability, the construction and inference of Bayesian networks, as well as applications in various fields such as bioinformatics and machine learning. Additionally, it delves into advanced topics such as temporal Bayesian networks and inference algorithms. This breadth of coverage ensures that both theoretical concepts and practical applications are addressed, making it a valuable resource. A researcher in machine learning, for instance, could leverage the advanced topics for developing predictive models.
  • question: How do Bayesian Networks differ from other statistical models?

    répondre: Bayesian networks differ from traditional statistical models by incorporating prior knowledge and beliefs into the analysis process. While frequentist methods mainly derive conclusions from sample data, Bayesian approaches utilize both prior distributions and observed data to provide a full probability distribution of the outcomes. This allows for more flexible modeling of uncertainty. For instance, in financial modeling, a Bayesian approach can adjust risk assessments dynamically as new data becomes available, unlike fixed predictive models.
  • question: Can Bayesian Networks be applied to real-world problems?

    répondre: Yes, Bayesian networks are widely applied to real-world problems across various domains such as healthcare, finance, and engineering. They facilitate decision-making under uncertainty by modeling complex relationships between different variables. For example, in public health, Bayesian networks can help in predictive analytics regarding disease outbreaks, guiding policymakers in allocating resources effectively. Similarly, in marketing, businesses can use these networks to understand customer behavior, enabling targeted marketing strategies.
  • question: What programming languages or tools are compatible with Bayesian Network implementations?

    répondre: Several programming languages and tools are compatible with Bayesian network implementations, including R, Python, and specialized software like Netica and GeNIe. R packages such as 'bnlearn' and 'gRain' provide comprehensive functionalities for building and analyzing Bayesian networks. In Python, libraries like 'pgmpy' allow for effective modeling and inference. This flexibility enables researchers and practitioners to choose the tools that best fit their workflow. A data scientist might use Python for integrating dynamic Bayesian network models into machine learning pipelines.
  • question: What are some common misconceptions about Bayesian Networks?

    répondre: One common misconception about Bayesian networks is that they require a vast amount of data to be effective, which is not always the case. They can perform well even with limited data, especially when prior knowledge is incorporated. Another misconception is that they are overly complex and hard to understand, whereas with practice, the graphical representation can simplify the understanding of complex relationships among variables. For example, a small start-up can model risks and dependencies accurately in project management, even without extensive datasets.
  • question: How do Bayesian Networks support decision-making processes?

    répondre: Bayesian networks support decision-making by providing a structured framework that visually represents the relationships and dependencies among variables. They allow decision-makers to update beliefs based on new evidence, guiding them towards more informed choices. For instance, in criminal justice, a Bayesian network can help predict the likelihood of recidivism based on various factors, aiding parole boards in making more evidence-based decisions regarding parole.
  • question: Where can I buy Bayesian Networks Chapman & Hall/CRC Texts in Statistical Science 2nd Edition?

    répondre: You can buy Bayesian Networks Chapman & Hall/CRC Texts in Statistical Science 2nd Edition from Ubuy. Ubuy offers a reliable platform for purchasing this book in Switzerland, along with user-friendly search features and a streamlined checkout process, ensuring you can find the right edition to enhance your understanding of Bayesian networks.

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CHF 71

Ce produit n'est pas livré par Ubuy et peut prendre au minimum 10 jours pour être livré. Il se peut que nous annulions le produit de la commande et que nous vous remboursions si un problème survient avec la livraison de ce produit.

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Caractéristiques et avantages

  • Step-by-step guide to Bayesian network modeling using R code
  • New material on modern machine-learning practices
  • Covers dynamic networks and arbitrary random variables using Stan
  • Provides an introductory overview of the field
  • Online supplementary materials include data sets and code