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Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Hardcover. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Sparse Graphical Modeling for High Dimensional Data : A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: New.
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Sparse Graphical Modeling for High Dimensional Data
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Sparse Graphical Modeling for High Dimensional Data : A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: New.
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Sparse Graphical Modeling for High Dimensional Data : A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: As New. Unread book in perfect condition.
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Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Published by Chapman and Hall/CRC 2023-08-02, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Hardcover. Condition: New.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Taylor & Francis Ltd, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. New copy - Usually dispatched within 4 working days. 185.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: New.
-
Sparse Graphical Modeling for High Dimensional Data : A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Sparse Graphical Modeling for High Dimensional Data (Hardcover)
Published by Taylor & Francis Ltd, London, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Hardcover. Condition: new. Hardcover. This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selectionEffective methods of high-dimensional inference This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Taylor & Francis Ltd, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
First Edition
Condition: New. 2023. 1st Edition. Hardcover. . . . . .
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SPARSE GRAPHICAL MODELING FOR HIGH DIMEN
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: NEW.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Hardcover. Condition: Brand New. 168 pages. 9.19x6.13x0.47 inches. In Stock.
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Sparse Graphical Modeling for High Dimensional Data
Published by Taylor and Francis Ltd, GB, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selectionEffective methods of high-dimensional inference.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Taylor & Francis Ltd, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: New. 2023. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Condition: New. Dr. Faming Liang is Distinguished Professor of Statistics, Purdue University. Prior joining Purdue University in 2017, he held regular faculty positions in the Department of Biostatistics, University of Florida and Department of Statisti.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Hardcover. Condition: Brand New. 168 pages. 9.19x6.13x0.47 inches. In Stock.
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Sparse Graphical Modeling for High Dimensional Data | A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Buch. Condition: Neu. Sparse Graphical Modeling for High Dimensional Data | A Paradigm of Conditional Independence Tests | Faming Liang (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2023 | Chapman and Hall/CRC | EAN 9780367183738 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Sparse Graphical Modeling for High Dimensional Data (Hardcover)
Published by Taylor & Francis Ltd, London, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Hardcover. Condition: new. Hardcover. This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selectionEffective methods of high-dimensional inference This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Sparse Graphical Modeling for High Dimensional Data
Published by Taylor and Francis Ltd, GB, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Hardback. Condition: New. This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selectionEffective methods of high-dimensional inference.
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Chapman and Hall/CRC, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Condition: New. PRINT ON DEMAND.
-
Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests
Published by Taylor & Francis Ltd, 2023
ISBN 10: 0367183730 ISBN 13: 9780367183738
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
