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Synopsis
Probability for Statistics and Machine Learning This accessible book provides a versatile treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It contains many worked out examples and exercises.
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Présentation de l'éditeur
This accessible book provides a versatile treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It contains many worked out examples and exercises.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurSpringer
- Date d'édition2013
- ISBN 10 146142884X
- ISBN 13 9781461428848
- ReliureBroché
- Langueanglais
- Nombre de pages804
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Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)
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Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)
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Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)
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Probability for Statistics and Machine Learning
Edité par
Springer New York, Springer US Jul 2013, 2013
ISBN 10 : 146142884X
ISBN 13 : 9781461428848
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability. 804 pp. Englisch. N° de réf. du vendeur 9781461428848
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Probability for Statistics and Machine Learning
Edité par
Springer New York, 2013
ISBN 10 : 146142884X
ISBN 13 : 9781461428848
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Unification of probability, statistics, and machine learning tools provides a complete background for teaching and future research inmultiple areasLucid and encyclopedic coverage allows the user to find and conceptually understand numerous topics . N° de réf. du vendeur 4197860
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Probability for Statistics and Machine Learning (Paperback)
Edité par
Springer-Verlag New York Inc., New York, NY, 2013
ISBN 10 : 146142884X
ISBN 13 : 9781461428848
Neuf
Paperback
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability. This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781461428848
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Probability for Statistics and Machine Learning : Fundamentals and Advanced Topics
Edité par
Springer New York, Springer US, 2013
ISBN 10 : 146142884X
ISBN 13 : 9781461428848
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability. N° de réf. du vendeur 9781461428848
Quantité disponible : 1 disponible(s)
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Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics
Edité par
Springer-Verlag New York Inc., 2013
ISBN 10 : 146142884X
ISBN 13 : 9781461428848
Neuf
Paperback / softback
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1241. N° de réf. du vendeur C9781461428848
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Probability for Statistics and Machine Learning (Paperback)
Edité par
Springer-Verlag New York Inc., New York, NY, 2013
ISBN 10 : 146142884X
ISBN 13 : 9781461428848
Neuf
Paperback
Vendeur : CitiRetail, Stevenage, Royaume-Uni
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Paperback. Etat : new. Paperback. This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability. This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781461428848
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Probability for Statistics and Machine Learning
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 804. N° de réf. du vendeur 2651417226
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