Inference and Learning from Data: Volume 2
Inference

Discover techniques for inferring unknown variables and quantities with the second volume of this extraordinary three-volume set.

Ali H. Sayed (Author)

9781009218269, Cambridge University Press

Hardback, published 22 December 2022

1070 pages
25.5 x 18 x 4 cm, 1.87 kg

'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt

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
Notation
27. Mean-Square-Error inference
28. Bayesian inference
29. Linear regression
30. Kalman filter
31. Maximum likelihood
32. Expectation maximization
33. Predictive modeling
34. Expectation propagation
35. Particle filters
36. Variational inference
37. Latent Dirichlet allocation
38. Hidden Markov models
39. Decoding HMMs
40. Independent component analysis
41. Bayesian networks
42. Inference over graphs
43. Undirected graphs
44. Markov decision processes
45. Value and policy iterations
46. Temporal difference learning
47. Q-learning
48. Value function approximation
49. Policy gradient methods
Author index
Subject index.

Subject Areas: Signal processing [UYS], Pattern recognition [UYQP], Machine learning [UYQM], Communications engineering / telecommunications [TJK], Information theory [GPF]