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E-raamat: Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine

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Theory Models and Applications to Finance Biology and Medicine.

This textbook, now in its third edition, offers a rigorous and self-contained introduction to the theory of continuous-time stochastic processes, stochastic integrals, and stochastic differential equations. Expertly balancing theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required.Key topics include:Markov processesStochastic differential equationsArbitrage-free markets and financial derivativesInsurance riskPopulation dynamics, and epidemicsAgent-based modelsNew to the Third Edition:Infinitely divisible distributionsRandom measuresLevy processesFractional Brownian motionErgodic theoryKarhunen-Loeve expansionAdditional applicationsAdditional exercisesSmoluchowski approximation of Langevin systemsAn Introduction to Continuous-Time Stochastic Processes, Third Edition will be of interest to a broad audience of students, pure and applied mathematicians, and researchers and practitioners in mathematical finance, biomathematics, biotechnology, and engineering. Suitable as a textbook for graduate or undergraduate courses, as well as European Masters courses (according to the two-year-long second cycle of the “Bologna Scheme”), the work may also be used for self-study or as a reference. Prerequisites include knowledge of calculus and some analysis; exposure to probability would be helpful but not required since the necessary fundamentals of measure and integration are provided.From reviews of previous editions:"The book is ... an account of fundamental concepts as they appear in relevant modern applications and literature. ... The book addresses three main groups: first, mathematicians working in a different field; second, other scientists and professionals from a business or academic background; third, graduate or advanced undergraduate students of a quantitative subject related to stochastic theory and/or applications." -Zentralblatt MATH

Arvustused

This is indeed a very well written book on stochastic processes and their numerous applications. The reader will definitely benefit from the exercises given at the end of each of the chapters. The book is strongly recommended to students following any graduate program in mathematics and mathematical modeling. University teachers can easily use this book as a possible reference book for special intermediate and advanced courses in stochastics and its applications. (Jordan M. Stoyanov, zbMATH 1333.60002, 2016)

Preface to the Third Edition v
Preface to the Second Edition vii
Preface ix
Part I Theory of Stochastic Processes
1 Fundamentals of Probability
3(74)
1.1 Probability and Conditional Probability
3(6)
1.2 Random Variables and Distributions
9(8)
1.2.1 Random Vectors
14(3)
1.3 Independence
17(3)
1.4 Expectations
20(10)
1.5 Gaussian Random Vectors
30(3)
1.6 Conditional Expectations
33(7)
1.7 Conditional and Joint Distributions
40(8)
1.8 Convergence of Random Variables
48(9)
1.9 Infinitely Divisible Distributions
57(9)
1.9.1 Examples
64(2)
1.10 Stable Laws
66(5)
1.10.1 Martingales
69(2)
1.11 Exercises and Additions
71(6)
2 Stochastic Processes
77(110)
2.1 Definition
77(8)
2.2 Stopping Times
85(1)
2.3 Canonical Form of a Process
86(1)
2.4 L2 Processes
87(4)
2.4.1 Gaussian Processes
88(1)
2.4.2 Karhunen-Loeve Expansion
89(2)
2.5 Processes with Independent Increments
91(3)
2.6 Markov Processes
94(19)
2.7 Martingales
113(16)
2.7.1 The martingale problem for Markov processes
124(5)
2.8 Brownian Motion and the Wiener Process
129(16)
2.9 Counting, and Poisson Processes
145(5)
2.10 Random Measures
150(3)
2.10.1 Poisson Random Measures
151(2)
2.11 Marked Counting Processes
153(11)
2.11.1 Counting Processes
153(3)
2.11.2 Marked Counting Processes
156(4)
2.11.3 The Marked Poisson Process
160(2)
2.11.4 Time-space Poisson Random Measures
162(2)
2.12 White Noise
164(1)
2.12.1 Gaussian white noise
164(1)
2.12.2 Poissonian white noise
165(1)
2.13 Levy Processes
165(13)
2.14 Exercises and Additions
178(9)
3 The Ito Integral
187(44)
3.1 Definition and Properties
187(12)
3.2 Stochastic Integrals as Martingales
199(5)
3.3 Ito Integrals of Multidimensional Wiener Processes
204(2)
3.4 The Stochastic Differential
206(4)
3.5 Ito's Formula
210(1)
3.6 Martingale Representation Theorem
211(3)
3.7 Multidimensional Stochastic Differentials
214(3)
3.8 The Ito Integral with Respect to Levy Processes
217(3)
3.9 The Ito-Levy Stochastic Differential and the Generalized Ito Formula
220(1)
3.10 Fractional Brownian Motion
221(4)
3.10.1 Integral with respect to a fBm
223(2)
3.11 Exercises and Additions
225(6)
4 Stochastic Differential Equations
231(50)
4.1 Existence and Uniqueness of Solutions
231(18)
4.2 Markov Property of Solutions
249(7)
4.3 Girsanov Theorem
256(4)
4.4 Kolmogorov Equations
260(10)
4.5 Multidimensional Stochastic Differential Equations
270(3)
4.6 Ito-Levy Stochastic Differential Equations
273(2)
4.6.1 Markov Property of Solutions of Ito-Levy Stochastic Differential Equations
275(1)
4.7 Exercises and Additions
275(6)
5 Stability, Stationarity, Ergodicity
281(32)
5.1 Time of explosion and regularity
283(3)
5.1.1 Application: A Stochastic Predator-Prey model
286(1)
5.2 Stability of Equilibria
286(5)
5.3 Stationary distributions
291(8)
5.3.1 Recurrence and transience
292(3)
5.3.2 Existence of a stationary distribution
295(1)
5.3.3 Ergodic theorems
296(3)
5.4 The one-dimensional case
299(8)
5.4.1 Stationary solutions
299(4)
5.4.2 First passage times
303(2)
5.4.3 Ergodic theorems
305(2)
5.5 Exercises and Additions
307(6)
Part II Applications of Stochastic Processes
6 Applications to Finance and Insurance
313(36)
6.1 Arbitrage-Free Markets
314(5)
6.2 The Standard Black--Scholes Model
319(7)
6.3 Models of Interest Rates
326(6)
6.4 Extensions and Alternatives to Black--Scholes
332(7)
6.5 Insurance Risk
339(6)
6.6 Exercises and Additions
345(4)
7 Applications to Biology and Medicine
349(52)
7.1 Population Dynamics: Discrete-in-Space--Continuous-in-Time Models
349(11)
7.2 Population Dynamics: Continuous Approximation of Jump Models
360(3)
7.3 Population Dynamics: Individual-Based Models
363(24)
7.3.1 A Mathematical Detour
367(2)
7.3.2 A "Moderate" Repulsion Model
369(7)
7.3.3 Ant Colonies
376(10)
7.3.4 Price Herding
386(1)
7.4 Neurosciences
387(5)
7.5 An SIS Epidemic Model
392(5)
7.5.1 Existence of a unique nonnegative solution
394(1)
7.5.2 Threshold theorem
394(2)
7.5.3 Stationary distribution
396(1)
7.6 Exercises and Additions
397(4)
Measure and Integration
401(18)
A.1 Rings and σ-Algebras
401(2)
A.2 Measurable Functions and Measure
403(3)
A.3 Lebesgue Integration
406(5)
A.4 Lebesgue--Stieltjes Measure and Distributions
411(4)
A.5 Radon Measures
415(1)
A.6 Stochastic Stieltjes Integration
416(3)
Convergence of Probability Measures on Metric Spaces
419(20)
B.1 Metric Spaces
419(11)
B.2 Prohorov's Theorem
430(1)
B.3 Donsker's Theorem
430(9)
Diffusion Approximation of a Langevin System
439(4)
Elliptic and Parabolic Equations
443(6)
D.1 Elliptic Equations
443(1)
D.2 The Cauchy Problem and Fundamental Solutions for Parabolic Equations
444(5)
Semigroups of Linear Operators 449(4)
Stability of Ordinary Differential Equations 453(4)
References 457(12)
Nomenclature 469(4)
Index 473
Vincenzo Capasso is a Professor of Probability and Mathematical Statistics at the University of Milan.  His research interests include spatially structured stochastic processes, stochastic geometry, reaction-diffusion systems, and statistics of structured stochastic processes. David Bakstein is a professor at the University of Milan, in ADAMSS (Interdisciplinary Center for Advanced Applied Mathematical and Statistical Sciences).