9781009288446 - scalable monte carlo for bayesian learning (institute of mathematical statistics monographs) de oates, chris j.; nemeth, christopher; fearnhead, paul (21 resultados)
Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
Paul Fearnhead , Christopher Nemeth , Chris J. Oates , Chris Sherlock
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Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Scalable Monte Carlo for Bayesian Learning
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Scalable Monte Carlo for Bayesian Learning
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Más imágenesScalable Monte Carlo for Bayesian Learning
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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Hardback. Condición: 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 technique…s 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.
Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
Fearnhead, Paul Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock,
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hardcover. Condición: New.
Scalable Monte Carlo for Bayesian Learning (Institute of Mathematical Statistics Monographs)
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Scalable Monte Carlo for Bayesian Learning
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Scalable Monte Carlo for Bayesian Learning
Fearnhead, Paul; Nemeth, Christopher; Oates, Chris J.; Sherlock, Chris
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Scalable Monte Carlo for Bayesian Learning
Paul Fearnhead , Christopher Nemeth , Chris J. Oates , Chris Sherlock
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 247 pages. 6.00x0.63x9.00 inches. In Stock.
Scalable Monte Carlo for Bayesian Learning
Paul Fearnhead , Christopher Nemeth , Chris J. Oates , Chris Sherlock
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Más imágenesScalable Monte Carlo for Bayesian Learning
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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Hardback. Condición: 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 technique…s 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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Hardcover. Condición: 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 n…ew 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.
Scalable Monte Carlo for Bayesian Learning
Paul Fearnhead , Christopher Nemeth , Chris J. Oates , Chris Sherlock
- Tapa dura
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 247 pages. 6.00x0.63x9.00 inches. In Stock. This item is printed on demand.
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Hardcover. Condición: 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 n…ew 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condición: 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 n…ew 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.
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Buch. Condición: 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.
