$990Hands-On Machine Learning with Scikit-Learn and TensorFlow (Paperback)
$960Deep Reinforcement Learning Hands-On
$3,658A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)
$1,758Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Paperback)
$2,166Math for Programmers: 3D graphics, machine learning, and simulations with Python (Paperback)
$1,480Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples (Paperback)
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.
- Provides all tools necessary to build and run realistic Bayesian network models
- 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, cybersecurity and more
- Introduces all necessary mathematics, probability, and statistics as needed
- Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications
A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.