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El. knyga: Orthogonal Polynomials in the Spectral Analysis of Markov Processes: Birth-Death Models and Diffusion

(Universidad Nacional Autónoma de México)
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"In pioneering work in the 1950s, S. Karlin and J. McGregor showed that probabilistic aspects of certain Markov processes can be studied by analyzing the orthogonal eigenfunctions of associated operators. In the decades since, many authors have extended and deepened this surprising connection between orthogonal polynomials and stochastic processes. This book gives a comprehensive analysis of the spectral representation of the most important one-dimensional Markov processes, namely discrete-time birth-death chains, birth-death processes and diffusion processes. It brings together all the main results from the extensive literature on the topic with detailed examples and applications. Also featuring an introduction to the basic theory of orthogonal polynomials and a selection of exercises at the end of each chapter, it is suitable for graduate students with a solid background in stochastic processes as well as researchers in orthogonal polynomials and special functions who want to learn about applications of their work to probability"--

Recenzijos

'The book serves as an excellent research monograph in this field and is strongly recommended by the reviewer to the researchers working in this field - both statisticians and mathematicians.' Lalit Mohan Upadhyaya, zbMATH Open

Daugiau informacijos

Gathers all the main results on the spectral representation of one-dimensional Markov processes, with examples and applications.
Preface ix
1 Orthogonal Polynomials
1(56)
1.1 Some Special Functions and the Stieltjes Transform
1(5)
1.2 General Properties of Orthogonal Polynomials
6(11)
1.3 The Spectral Theorem for Orthogonal Polynomials
17(7)
1.4 Classical Orthogonal Polynomials of a Continuous Variable
24(16)
1.5 Classical Orthogonal Polynomials of a Discrete Variable
40(10)
1.6 The Askey Scheme
50(3)
1.7 Exercises
53(4)
2 Spectral Representation of Discrete-Time Birth-Death Chains
57(89)
2.1 Discrete-Time Markov Chains
58(5)
2.2 Karlin-McGregor Representation Formula
63(8)
2.3 Properties of the Birth-Death Polynomials and Other Related Families
71(13)
2.4 Examples
84(20)
2.5 Applications to the Probabilistic Aspects of Discrete-Time Birth-Death Chains
104(28)
2.6 Discrete-Time Birth-Death Chains on the Integers
132(11)
2.7 Exercises
143(3)
3 Spectral Representation of Birth-Death Processes
146(108)
3.1 Continuous-Time Markov Chains
147(13)
3.2 Karlin-McGregor Representation Formula
160(6)
3.3 Properties of the Birth-Death Polynomials and Other Related Families
166(14)
3.4 The Karlin-McGregor Formula as a Transition Probability Function
180(7)
3.5 Birth-Death Processes with Killing
187(4)
3.6 Examples
191(24)
3.7 Applications to the Probabilistic Aspects of Birth-Death Processes
215(28)
3.8 Bilateral Birth-Death Processes
243(6)
3.9 Exercises
249(5)
4 Spectral Representation of Diffusion Processes
254(68)
4.1 Diffusion Processes
255(7)
4.2 Spectral Representation of the Transition Probability Density
262(6)
4.3 Classification of Boundary Points
268(8)
4.4 Diffusion Processes with Killing
276(3)
4.5 Examples
279(33)
4.6 Quasi-Stationary Distributions
312(8)
4.7 Exercises
320(2)
References 322(9)
Index 331
Manuel Domķnguez de la Iglesia is Professor of Mathematics at the Instituto de Matemįticas of the Universidad Nacional Autónoma de México.