Overview

In a world where data flows endlessly and decisions must be made in real time, traditional batch learning techniques fall short. Hybrid Probabilistic Graphical Models for Streaming Data in R: Theory and Applications offers readers a comprehensive exploration of how probabilistic graphical models (PGMs) can be adapted, extended, and applied to the challenges of streaming data environments. This book guides readers from foundational principles to advanced methods, presenting hybrid PGMs as powerful tools for modeling uncertainty, dependency structures, and dynamic relationships in data that never stops moving. With a balance of rigorous theory and practical examples in R, it shows how Bayesian networks, Markov models, and dynamic PGMs can be combined and optimized to handle high-volume, high-velocity, and high-variety datasets. Designed for both researchers and practitioners, the book covers: The core building blocks of PGMs and their hybrid extensions. Techniques for real-time inference, parameter estimation, and structure learning in streaming contexts. Scalable algorithms for handling non-stationary data, concept drift, and noise. Practical implementations in R with reproducible code examples. Applications across finance, healthcare, cybersecurity, IoT, and social media analytics. Whether you are a data scientist, statistician, or researcher, this book provides the tools and insights to design models that adapt as data evolves. Readers will not only master the mathematics behind hybrid PGMs but also gain hands-on skills in applying them to real-world problems using R. In an era where information is both abundant and fleeting, this book equips you to make sense of uncertainty-one stream at a time.

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