Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Añadir al carritoHardcover. Condición: Good. Clean pages. Some spine separation. Boards unmarked.
Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Añadir al carritoPaperback. 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press 2022-12-22, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Añadir al carritoHardcover. Condición: New.
Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Añadir al carritoHardcover. Condición: Brand New. 1070 pages. 9.88x7.24x1.77 inches. In Stock.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
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Añadir al carritoCondición: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.
Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 138,82
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Añadir al carritoCondición: New. New edition niversity Press NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: moluna, Greven, Alemania
EUR 105,28
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Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 110,75
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: preigu, Osnabrück, Alemania
EUR 116,90
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Añadir al carritoBuch. Condición: Neu. Inference and Learning from Data | Ali H. Sayed | Buch | Gebunden | Englisch | 2022 | Cambridge University Press | EAN 9781009218269 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: Revaluation Books, Exeter, Reino Unido
EUR 176,02
Cantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. 1070 pages. 9.88x7.24x1.77 inches. In Stock.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 133,53
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Añadir al carritoHardcover. Condición: new. Hardcover. This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference. Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: CitiRetail, Stevenage, Reino Unido
EUR 108,85
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: new. Hardcover. This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference. Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: Majestic Books, Hounslow, Reino Unido
EUR 143,45
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. Print on Demand.
Idioma: Inglés
Publicado por Cambridge University Press, 2023
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 146,80
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. PRINT ON DEMAND.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1009218263 ISBN 13: 9781009218269
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 145,47
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: new. Hardcover. This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference. Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering. 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.