Inference in Statistical Modelling and Machine Learning

James Burridge

ISBN 10: 1009630725 / ISBN 13: 9781009630726

Published by Cambridge University Press, 2026

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Statistical modelling and machine learning offer a vast toolbox of inference methods with which to model the world, discover patterns and reach beyond the data to make predictions when the truth is not certain. This concise book provides a clear introduction to those tools and to the core ideas - probabilistic model, likelihood, prior, posterior, overfitting, underfitting, cross-validation - that unify them. Toy and real examples illustrate diverse applications ranging from biomedical data to treasure hunts, while the accompanying datasets and computational not Elektronisches Buch in R and Python encourage hands-on learning. Instructors can benefit from online lecture slides and solutions to all the exercises. Requiring only first-year university-level knowledge of calculus, probability and linear algebra, the book equips students in statistics, data science and machine learning, as well as those in quantitative applied and social science programmes, with the tools and conceptual foundations to explore more advanced techniques.

Seller Inventory # 9781009630726

Bibliographic Details

• Title: Inference in Statistical Modelling and Machine Learning
• Publisher: Cambridge University Press
• Publication Date: 2026
• Language: English
• ISBN 10: 1009630725
• ISBN 13: 9781009630726
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 18D millimeters 254 millimeters width by 178 millimeters height by 18 millimeters depth
• Weight: 608 grams

About this title

Synopsis

Statistical modelling and machine learning offer a vast toolbox of inference methods with which to model the world, discover patterns and reach beyond the data to make predictions when the truth is not certain. This concise book provides a clear introduction to those tools and to the core ideas – probabilistic model, likelihood, prior, posterior, overfitting, underfitting, cross-validation – that unify them. Toy and real examples illustrate diverse applications ranging from biomedical data to treasure hunts, while the accompanying datasets and computational notebooks in R and Python encourage hands-on learning. Instructors can benefit from online lecture slides and solutions to all the exercises. Requiring only first-year university-level knowledge of calculus, probability and linear algebra, the book equips students in statistics, data science and machine learning, as well as those in quantitative applied and social science programmes, with the tools and conceptual foundations to explore more advanced techniques.

About the Authors

James Burridge is Professor of Probability and Statistical Physics at the University of Portsmouth, where he teaches probability, stochastic processes and statistical learning. He models language, birdsong, rocks, tessellations and games, and develops commercial applications of machine learning in green technology.

Nick Tosh is Lecturer in Philosophy at the University of Galway, where he teaches critical thinking, logic and the philosophy of science. Until 2024, he coordinated Galway's Arts with Data Science BA.