measure theory analysis probability di goswami alok (19 risultati)

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. It begins by demonstrating the essential nature of measure theory before delving into the construction of measures and the development of integration theory. Special attention is given to probability spaces and random variables/vectors. The text then explores product spaces, RadonNikodym and JordanHahn theorems, providing a detailed account of This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: New. In.

Condizione: New.

hardcover. Condizione: New.

Hardback. Condizione: New. 2024 ed. This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. It begins by demonstrating the essential nature of measure theory before delving into the construction of measures and the development of integration theory. Special attention is given to probability spaces and random variables/vectors. The text then explores product spaces, Radon-Nikodym and Jordan-Hahn theorems, providing a detailed account of  spaces and their duals. After revisiting probability theory, it discusses standard limit theorems such as the laws of large numbers and the central limit theorem, with detailed treatment of weak convergence and the role of characteristic functions.The book further explores conditional probabilities and expectations, preceded by motivating discussions. It discusses the construction of probability measures on infinite product spaces, presenting Tulcea's theorem and Kolmogorov's consistency theorem. The text concludes with the construction of Brownian motion, examining its path properties and the significant strong Markov property. This comprehensive guide is invaluable not only for those pursuing probability theory seriously but also for those seeking a robust foundation in measure theory to advance in modern analysis. By effectively motivating readers, it underscores the critical role of measure theory in grasping fundamental probability concepts.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 2024th edition NO-PA16APR2015-KAP.

Condizione: New.

Hardcover. Condizione: new. Hardcover. This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. It begins by demonstrating the essential nature of measure theory before delving into the construction of measures and the development of integration theory. Special attention is given to probability spaces and random variables/vectors. The text then explores product spaces, RadonNikodym and JordanHahn theorems, providing a detailed account of This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New.

Hardcover. Condizione: new. Hardcover. This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. It begins by demonstrating the essential nature of measure theory before delving into the construction of measures and the development of integration theory. Special attention is given to probability spaces and random variables/vectors. The text then explores product spaces, RadonNikodym and JordanHahn theorems, providing a detailed account of This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardback. Condizione: New. 2024 ed. This book covers major measure theory topics with a fairly extensive study of their applications to probability and analysis. It begins by demonstrating the essential nature of measure theory before delving into the construction of measures and the development of integration theory. Special attention is given to probability spaces and random variables/vectors. The text then explores product spaces, Radon-Nikodym and Jordan-Hahn theorems, providing a detailed account of  spaces and their duals. After revisiting probability theory, it discusses standard limit theorems such as the laws of large numbers and the central limit theorem, with detailed treatment of weak convergence and the role of characteristic functions.The book further explores conditional probabilities and expectations, preceded by motivating discussions. It discusses the construction of probability measures on infinite product spaces, presenting Tulcea's theorem and Kolmogorov's consistency theorem. The text concludes with the construction of Brownian motion, examining its path properties and the significant strong Markov property. This comprehensive guide is invaluable not only for those pursuing probability theory seriously but also for those seeking a robust foundation in measure theory to advance in modern analysis. By effectively motivating readers, it underscores the critical role of measure theory in grasping fundamental probability concepts.

Hardcover. Condizione: Brand New. 387 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers major measure theory topics with a fairly extensivestudy of their applications toprobability and analysis.It begins by demonstrating the essential nature of measure theory before delving into the construction of measures and the development of integration theory. Special attention is given to probability spaces and random variables/vectors. The text then explores product spaces, Radon-Nikodym and Jordan-Hahn theorems, providing a detailed account of spaces and their duals. After revisiting probability theory, it discusses standard limit theorems such as the laws of large numbers and the central limit theorem, with detailed treatment of weak convergence and the role of characteristic functions.The book further explores conditional probabilities and expectations, preceded by motivating discussions. It discusses the construction of probability measures on infinite product spaces, presenting Tulcea's theorem and Kolmogorov's consistency theorem. The text concludes with the construction of Brownian motion, examining its path properties and the significant strong Markov property. This comprehensive guide is invaluable not only for those pursuing probability theory seriously but also for those seeking a robust foundation in measure theory to advance in modern analysis. By effectively motivating readers, it underscores the critical role of measure theory in grasping fundamental probability concepts. 377 pp. Englisch.

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