Textbook in PDF format

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable MatLAB code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Preface
P.1 Emphasis on Foundations
P.2 Glimpse of History
P.3 Organization of the Text
P.4 How to Use the Text
P.5 Simulation Datasets
P.6 Acknowledgments
Notation
Mean-Square-Error Inference
Bayesian Inference
Linear Regression
Kalman Filter
Maximum Likelihood
Expectation Maximization
Predictive Modeling
Expectation Propagation
Particle Filters
Variational Inference
Latent Dirichlet Allocation
Hidden Markov Models
Decoding Hidden Markov Models
Independent Component Analysis
Bayesian Networks
Inference over Graphs
Undirected Graphs
Markov Decision Processes
Value and Policy Iterations
Temporal Difference Learning
Q-Learning
Value Function Approximation
Policy Gradient Methods
Author Index
Subject Index