9780691197296 - statistical inference via convex optimization di juditsky, anatoli; nemirovski, arkadi (21 risultati)
Lingua: Inglese
Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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hardcover. Condizione: Very Good. Very Good. Used with some reading wear but is still in great reading condition. No markings in text.
Lingua: Inglese
Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Editore: Princeton University Press, 2020
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Condizione: New. 2020. Hardcover. . . . . .
Lingua: Inglese
Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Lingua: Inglese
Editore: Princeton University Press, US, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Hardback. Condizione: New. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory ca…n be used to devise and analyze near-optimal statistical inferences.Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Lingua: Inglese
Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Condizione: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
Lingua: Inglese
Editore: Princeton University Press 2020-04-07, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Lingua: Inglese
Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Lingua: Inglese
Editore: Princeton University Press, New Jersey, 2020
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Hardcover. Condizione: new. Hardcover. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimizati…on theory can be used to devise and analyze near-optimal statistical inferences.Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal st Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: Princeton University Press, US, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
- Rilegato
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Hardback. Condizione: New. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory ca…n be used to devise and analyze near-optimal statistical inferences.Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Lingua: Inglese
Editore: Princeton Univ Pr, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Hardcover. Condizione: Brand New. 631 pages. 10.25x7.25x1.25 inches. In Stock.
Lingua: Inglese
Editore: Princeton University Press, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Editore: Princeton University Press, US, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
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Hardback. Condizione: New. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory ca…n be used to devise and analyze near-optimal statistical inferences.Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Lingua: Inglese
Editore: Princeton University Press, US, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
- Rilegato
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Hardback. Condizione: New. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory ca…n be used to devise and analyze near-optimal statistical inferences.Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
Lingua: Inglese
Editore: Princeton University Press, New Jersey, 2020
Serie: Libro 32 di 33 - Princeton Series in Applied Mathematics
- Rilegato
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Hardcover. Condizione: new. Hardcover. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimizati…on theory can be used to devise and analyze near-optimal statistical inferences.Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems.Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text. This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal st Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
