Inference and Learning from Data: Volume 2: Inference

This three-volume work is a comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference, with a focus on inference techniques such as Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. It is ideal for early-career researchers and graduate students in signal processing, machine learning, statistical analysis, data science, and inference.

Format: Hardback
Length: 1070 pages
Publication date: 22 December 2022
Publisher: Cambridge University Press

This remarkable three-volume work, penned by a foremost authority in the field, presents an accessible and comprehensive introduction to the diverse range of mathematical and statistical techniques that form the foundation of contemporary methods in data-driven learning and inference. The second volume in this series, titled Inference, builds upon the foundational topics established in the first volume, delving into advanced techniques for inferring unknown variables and quantities. These techniques encompass Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogical approach are employed throughout this volume to enhance student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, nearly 200 figures, datasets, and downloadable Matlab code. Supported by sister volumes Foundations and Learning, this textbook sequence stands out for its extensive scope and depth, making it an ideal resource for early-career researchers and graduate students pursuing courses in signal processing, machine learning, statistical analysis, data science, and inference.

Weight: 1872g
Dimension: 151 x 252 x 46 (mm)
ISBN-13: 9781009218269