Inference and learning from data, Vol. 2: inference

By:

Sayed, Ali H

Publication details: Cambridge, UK: Cambridge University Press, 2023.Description: li, 1053p.-2164p.: col. ill.; hbk.: 25cmISBN:

9781009218269

Subject(s):

DDC classification:

519.54 SAY

Summary: 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. Unique in its scale and depth, this is a comprehensive introduction to methods in data-driven learning and inference Over 1300 end-of-chapter problems (with solutions for instructors), 600 figures and 470 in-text solved examples across the three volumes A phenomenal contribution by a world authority in the field Covers sufficient topics across the volumes for the construction of a variety of courses covering a wide range of themes https://www.cambridge.org/in/universitypress/subjects/engineering/communications-and-signal-processing/inference-and-learning-data-inference-volume-2?format=HB&isbn=9781009218269

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	519.54 SAY (Browse shelf(Opens below))	2	Available		033243

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Previous	519.535 KLE Applied regression analysis and other multivariable methods, 5th ed.	519.535 LAT Analyzing multivariate data	519.54 SAY Inference and learning from data, Vol. 1: foundations	519.54 SAY Inference and learning from data, Vol. 2: inference	519.54 SAY Inference and learning from data, Vol. 3: learning	519.542 SIL Signal and the noise: the art and science of prediction	519.542 TOR Comparison of statistical experiments, Vol. 36	Next

Includes Author Index and Subject Index

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.

Unique in its scale and depth, this is a comprehensive introduction to methods in data-driven learning and inference
Over 1300 end-of-chapter problems (with solutions for instructors), 600 figures and 470 in-text solved examples across the three volumes
A phenomenal contribution by a world authority in the field
Covers sufficient topics across the volumes for the construction of a variety of courses covering a wide range of themes

https://www.cambridge.org/in/universitypress/subjects/engineering/communications-and-signal-processing/inference-and-learning-data-inference-volume-2?format=HB&isbn=9781009218269

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