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Synopsis
A concise introduction to core ideas in statistical modelling and machine learning, preparing you to progress to more advanced techniques.
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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. He has published on methodological disputes in the history of science and on the interpretation of probability. Until 2024, he coordinated Galway's Arts with Data Science BA.
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Inference in Statistical Modelling and Machine Learning (Paperback)
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Cambridge University Press, Cambridge, 2026
ISBN 10: 1009630725
ISBN 13: 9781009630726
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Paperback. Condition: new. Paperback. 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. This concise introduction to statistical modelling and machine learning focuses on core ideas and a carefully selected set of representative methods. Requiring only introductory calculus, probability and linear algebra, it provides readers with an immediately useful toolkit and equips them to consult more advanced resources. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009630726
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Inference in Statistical Modelling and Machine Learning
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ISBN 10: 1009630725
ISBN 13: 9781009630726
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Inference In Statistical Modelling And Machine Learning: A Concise Introduction
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ISBN 10: 1009630725
ISBN 13: 9781009630726
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Inference in Statistical Modelling and Machine Learning: A Concise Introduction
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ISBN 10: 1009630725
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Inference in Statistical Modelling and Machine Learning: A Concise Introduction
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ISBN 10: 1009630725
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Inference In Statistical Modelling And Machine Learning: A Concise Introduction
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ISBN 10: 1009630725
ISBN 13: 9781009630726
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Inference in Statistical Modelling and Machine Learning: A Concise Introduction
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Inference in Statistical Modelling and Machine Learning: A Concise Introduction
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Inference in Statistical Modelling and Machine Learning: A Concise Introduction
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ISBN 10: 1009630725
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