Bayesian Networks
A Practical Guide to Probabilistic Reasoning and Causal Modeling
Many people believe that mastering probability and statistics is enough to handle uncertainty in complex systems. Yet when faced with real-world problems involving multiple interacting variables, incomplete information, and the need to reason about causes and effects, traditional statistical methods often fall short. You need a framework that can represent intricate dependencies, update beliefs as new evidence emerges, and distinguish genuine causal relationships from mere correlations. Bayesian networks offer exactly this capability. This book guides you through the theory and practice of building, analyzing, and applying Bayesian networks to solve challenging problems. You'll discover how to construct networks that capture domain knowledge, perform efficient probabilistic inference, learn network structures from data, and use these models for prediction and decision-making. Through clear explanations and practical examples, you'll gain the skills to apply Bayesian networks across diverse domains—from diagnostic systems to risk assessment, from machine learning to causal analysis. Whether you're working with complete or incomplete data, simple or complex dependencies, you'll learn how to harness the power of probabilistic graphical models to reason systematically under uncertainty.
Est. Length
253 pages (12 chapters)
Language
English
Publication date
Oct 23, 2025
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