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ISBN 10: 100928844X ISBN 13: 9781009288446
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Lingua: Inglese
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Aggiungi al carrelloHardback. Condizione: 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.
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Aggiungi al carrelloHardback. Condizione: 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.
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Aggiungi al carrelloHardcover. Condizione: 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
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Aggiungi al carrelloHardback. Condizione: 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.
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Editore: Cambridge University Press, 2025
ISBN 10: 100928844X ISBN 13: 9781009288446
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2025
ISBN 10: 100928844X ISBN 13: 9781009288446
Da: Rarewaves.com UK, London, Regno Unito
EUR 74,91
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Aggiungi al carrelloHardback. Condizione: 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.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2025
ISBN 10: 100928844X ISBN 13: 9781009288446
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: 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.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 100928844X ISBN 13: 9781009288446
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: Brand New. 247 pages. 6.00x0.63x9.00 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2025
ISBN 10: 100928844X ISBN 13: 9781009288446
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Aggiungi al carrelloHardcover. Condizione: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 100928844X ISBN 13: 9781009288446
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Aggiungi al carrelloBuch. Condizione: Neu. Scalable Monte Carlo for Bayesian Learning | Paul Fearnhead (u. a.) | Buch | Englisch | 2025 | Cambridge University Press | EAN 9781009288446 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.