Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs) - Hardcover
Synopsis
A clear and intuitive introduction to advanced topics in Markov chain Monte Carlo, with a focus on scalability.
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About the Authors
Paul Fearnhead is Professor of Statistics at Lancaster University, with research interests in Bayesian and Computational Statistics. He has been awarded Cambridge University's Adams prize, and the Guy Medals in Bronze and Silver from the Royal Statistical Society. He was elected a fellow of the International Society for Bayesian Analysis in 2024 and is currently the Editor of Biometrika.
Christopher Nemeth is Professor of Statistics at Lancaster University, working at the interface of Statistics and Machine Learning, with a focus on probabilistic modelling and the development of new computational tools for statistical inference. In 2020, he was awarded a UKRI Turing AI Fellowship to develop new algorithms for probabilistic AI.
Chris. J. Oates leads a team working in the areas of Computational Statistics and Probabilistic Machine Learning at Newcastle University. He was awarded a Leverhulme Prize for Mathematics and Statistics in 2023, and the Guy Medal in Bronze of the Royal Statistical Society in 2024.
Chris Sherlock is Professor of Statistics at Lancaster University. After working in data assimilation, numerical modelling and software engineering, he was caught up in the excitement of Computationally Intensive Bayesian Statistics, obtaining a Ph.D. in the topic and now leading a group of like-minded researchers.
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Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
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Scalable Monte Carlo for Bayesian Learning
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Scalable Monte Carlo for Bayesian Learning (Hardcover)
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Hardcover. Condition: new. Hardcover. A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field. An intuitive introduction to advanced topics in Markov chain Monte Carlo (MCMC), presenting cutting-edge developments that address the crucial issue of scalability. It could form the basis for a graduate-level course and will be a valuable resource for researchers in the field. 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 # 9781009288446
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Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
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Hardback. Condition: New. A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field. Seller Inventory # LU-9781009288446
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Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
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