Probabilistic Graphical Models

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The book offers a comprehensive introduction to probabilistic graphical models (PGMs) tailored for engineering students. This updated edition includes fresh insights into partially observable Markov decision processes, causal graphical models, and deep learning. Enhanced with numerous exercises, it also features a software library for implementing various graphical models in Python, making it a practical resource for both learning and application.