Probability Measure, First Edition (18 results)

Hardcover. Condition: Good. Dust Jacket Condition: No Dust Jacket. First Edition. 110 pages. Some wear to the spine and cover edges, lightly toned pages; a solid reading copy. No jacket. Quantity Available: 1. Category: Mathematics; Inventory No: 230488.

Soft cover. Condition: As New. 1st Edition. This is a fine, as new, obviously unread, first edition paperback copy, blue spine, 351 pages with index.

Hard Cover. Condition: Fair. No Jacket. First Edition. From an academic library with the usual stamps etc. A rather worn working copy.

Soft Covers. Condition: Near Fine. First Edition. Springer Undergraduate Mathematics Series. Yellow, stiff card covers. Pp xi, 227. An ex university copy with a couple of the usual signifiers but the book is hardly used and in excellent condition. Further info. available upon request.

Paperback. Condition: New. 1st. Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

Cloth. Condition: Good. Dust Jacket Condition: Good. First Edition. Type: Book N.B. Previous owners signature to inside front cover. Fading to D/J spine. Rubbing to edges and corners of D/J with small tear to bottom edge of rear D/J.

Hardcover. Condition: New. No Jacket. 1st Edition. This is a new hardcover first edition copy, slick red binding, no DJ, 360 pages with index. Photos on request.

Condition: New. The authors believe that a proper treatment of probability theory requires an adequate background in the theory of finite measures in general spaces. Num Pages: 416 pages, black & white illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 25. Weight in Grams: 61. . 2009. 1st Edition. paperback. . . . .

Paperback. Condition: New. 1st. Rigorous probabilistic arguments, built on the foundation of measure theory introduced eighty years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This 2002 book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

Soft cover. Condition: Near Fine. No Jacket. 1st Edition. Near Fine / --. First printing of first edition. Vol.13 in the Advanced Series on Statistical Science & Applied Probability. Pictorial card covers. Binding is very firm and tight. Interior is very clean with no inscriptions.Not ex-library.

Hardcover. Condition: new. Hardcover. An introduction to the mathematical theory and financial models developed and used on Wall Street Providing both a theoretical and practical approach to the underlying mathematical theory behind financial models, Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach presents important concepts and results in measure theory, probability theory, stochastic processes, and stochastic calculus. Measure theory is indispensable to the rigorous development of probability theory and is also necessary to properly address martingale measures, the change of numeraire theory, and LIBOR market models. In addition, probability theory is presented to facilitate the development of stochastic processes, including martingales and Brownian motions, while stochastic processes and stochastic calculus are discussed to model asset prices and develop derivative pricing models. The authors promote a problem-solving approach when applying mathematics in real-world situations, and readers are encouraged to address theorems and problems with mathematical rigor. In addition, Measure, Probability, and Mathematical Finance features: A comprehensive list of concepts and theorems from measure theory, probability theory, stochastic processes, and stochastic calculusOver 500 problems with hints and select solutions to reinforce basic concepts and important theoremsClassic derivative pricing models in mathematical finance that have been developed and published since the seminal work of Black and Scholes Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach is an ideal textbook for introductory quantitative courses in business, economics, and mathematical finance at the upper-undergraduate and graduate levels. The book is also a useful reference for readers who need to build their mathematical skills in order to better understand the mathematical theory of derivative pricing models. Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach features an introduction to the mathematical theory underlying the financial models that were developed and employed on Wall Street. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach features an introduction to the mathematical theory underlying the financial models that were developed and employed on Wall Street. Num Pages: 744 pages. BIC Classification: KFF; PBT; PBW. Category: (P) Professional & Vocational. Dimension: 242 x 157 x 42. Weight in Grams: 1120. . 2014. 1st Edition. hardcover. . . . .

Hardcover. Condition: new. Hardcover. An introduction to the mathematical theory and financial models developed and used on Wall Street Providing both a theoretical and practical approach to the underlying mathematical theory behind financial models, Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach presents important concepts and results in measure theory, probability theory, stochastic processes, and stochastic calculus. Measure theory is indispensable to the rigorous development of probability theory and is also necessary to properly address martingale measures, the change of numeraire theory, and LIBOR market models. In addition, probability theory is presented to facilitate the development of stochastic processes, including martingales and Brownian motions, while stochastic processes and stochastic calculus are discussed to model asset prices and develop derivative pricing models. The authors promote a problem-solving approach when applying mathematics in real-world situations, and readers are encouraged to address theorems and problems with mathematical rigor. In addition, Measure, Probability, and Mathematical Finance features: A comprehensive list of concepts and theorems from measure theory, probability theory, stochastic processes, and stochastic calculusOver 500 problems with hints and select solutions to reinforce basic concepts and important theoremsClassic derivative pricing models in mathematical finance that have been developed and published since the seminal work of Black and Scholes Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach is an ideal textbook for introductory quantitative courses in business, economics, and mathematical finance at the upper-undergraduate and graduate levels. The book is also a useful reference for readers who need to build their mathematical skills in order to better understand the mathematical theory of derivative pricing models. Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach features an introduction to the mathematical theory underlying the financial models that were developed and employed on Wall Street. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: Fine. Dust Jacket Condition: VG+. First Edition. 202 pages in excellent condition. Clean endpapers. Green hardcovers with gilt titles on the spine. Corners not bumped. Illustrated DJ with green/red titles. Very light wear on corners and edges. FINE/VG+. Book.

Hardcover. Condition: new. Hardcover. Rigorous probabilistic arguments, built on the foundation of measure theory introduced seventy years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form.It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean. This book offers a rigorous probability course. It covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms plus more advanced topics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. Rigorous probabilistic arguments, built on the foundation of measure theory introduced seventy years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form.It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean. This book offers a rigorous probability course. It covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms plus more advanced topics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. Rigorous probabilistic arguments, built on the foundation of measure theory introduced seventy years ago by Kolmogorov, have invaded many fields. Students of statistics, biostatistics, econometrics, finance, and other changing disciplines now find themselves needing to absorb theory beyond what they might have learned in the typical undergraduate, calculus-based probability course. This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form.It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean. This book offers a rigorous probability course. It covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms plus more advanced topics. 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.

Hardcover. Condition: new. Hardcover. An introduction to the mathematical theory and financial models developed and used on Wall Street Providing both a theoretical and practical approach to the underlying mathematical theory behind financial models, Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach presents important concepts and results in measure theory, probability theory, stochastic processes, and stochastic calculus. Measure theory is indispensable to the rigorous development of probability theory and is also necessary to properly address martingale measures, the change of numeraire theory, and LIBOR market models. In addition, probability theory is presented to facilitate the development of stochastic processes, including martingales and Brownian motions, while stochastic processes and stochastic calculus are discussed to model asset prices and develop derivative pricing models. The authors promote a problem-solving approach when applying mathematics in real-world situations, and readers are encouraged to address theorems and problems with mathematical rigor. In addition, Measure, Probability, and Mathematical Finance features: A comprehensive list of concepts and theorems from measure theory, probability theory, stochastic processes, and stochastic calculusOver 500 problems with hints and select solutions to reinforce basic concepts and important theoremsClassic derivative pricing models in mathematical finance that have been developed and published since the seminal work of Black and Scholes Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach is an ideal textbook for introductory quantitative courses in business, economics, and mathematical finance at the upper-undergraduate and graduate levels. The book is also a useful reference for readers who need to build their mathematical skills in order to better understand the mathematical theory of derivative pricing models. Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach features an introduction to the mathematical theory underlying the financial models that were developed and employed on Wall Street. 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.