Last edited by Moogushakar

Thursday, July 16, 2020 | History

8 edition of **Stochastic Processes and Orthogonal Polynomials** found in the catalog.

- 364 Want to read
- 33 Currently reading

Published
**April 27, 2000**
by Springer
.

Written in English

- Probability & statistics,
- Reference works,
- Stochastics,
- Probability & Statistics - General,
- Polynomials,
- Mathematics,
- Medical / Nursing,
- Science/Mathematics,
- General,
- Mathematics / Statistics,
- Mathematics-Probability & Statistics - General,
- Medical / General,
- Algebra - Elementary,
- Orthogonal polynomials,
- Stochastic Processes

**Edition Notes**

Lecture Notes in Statistics

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 163 |

ID Numbers | |

Open Library | OL9349206M |

ISBN 10 | 038795015X |

ISBN 10 | 9780387950150 |

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes . The program for the San Antonio meeting gives an indication of this: there were papers devoted to applications of orthogonal polynomials to partition theory, combinatorics, sphere packing, stochastic processes, X-ray tomography, quantum scattering theory and nuclear by: 3.

Topics in Random Polynomials presents a rigorous and comprehensive treatment of the mathematical behavior of different types of random polynomials. These polynomials-the subject of extensive recent research-have many applications in physics, economics, and statistics. The main results are presented in such a fashion that they can be understood and used by readers whose knowledge of probability. Orthogonal polynomials are essential tools for the solution of many problems in the spectral theory of differential and difference equations, Painlevé equations (discrete and continuous versions), numerical methods in quadrature on the real line and the unit circle, as well as cubature formulas on multidimensional domains, with applications.

In contrast, when Hermite polynomials are used, the PC eigenfrequencies spread from the deterministic eigenfrequencies (the highest roots of the Hermite polynomials tend to infinity when the order tends to infinity). Consequently, when the PC number . signiﬁcance for many classical families of orthogonal polynomials. Starting with a stochastic process and using the stochastic measures machinery introduced by Rota and Wallstrom, we calculate and give an interpretation of linearization coeﬃcients for a number of polyno-mial families. The processes involved may have independent,freely in-.

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The book offers an accessible reference for researchers in the probability, statistics and special functions communities. It gives a variety of interdisciplinary relations between the two main ingredients of stochastic processes and orthogonal by: The main focus of this book Stochastic Processes and Orthogonal Polynomials book the relationship between orthogonal polynomials and stochastic processes.

In this chapter we review the relevant background of orthogonal polynomials. Get this from a library. Stochastic processes and orthogonal polynomials. [Wim Schoutens] -- "This volume clearly illustrates the powerful mathematical role of orthogonal polynomials in the analysis of stochastic processes and is accessible for those with a basic background in probability.

ISBN: X OCLC Number: Description: 1 v. (XIII p.) ; 24 cm. Contents: 1 The Askey Scheme of Orthogonal Polynomials.- Markov Processes.- 3 Birth and Death Processes, Random Walks, and Orthogonal Polynomials.- 4 Sheffer Systems.- 5 Orthogonal Polynomials in Stochastic Integration Theory Stochastic Processes and Orthogonal Polynomials It seems that you're in USA.

We have a dedicated Get immediate ebook access* when you order a print book Mathematics Probability Theory and Stochastic Processes. Orthogonal Polynomials in Stochastic Integration : Springer-Verlag New York.

The book offers an accessible reference for researchers in the probabi lity, statistics and special functions communities. It gives a variety of interdisciplinary relations between the two main ingredients of st ochastic processes and orthogonal polynomials.

It covers topics like t ime Price: $ Stochastic Processes and Orthogonal Polynomials. Authors (view affiliations) Wim Schoutens; Book. Search within book.

Front Matter. Pages i-xiii. PDF. The Askey Scheme of Orthogonal Polynomials Wim Schoutens. Pages Orthogonal Polynomials in Stochastic Integration Theory. Wim Schoutens. Pages Stein Approximation and. One important type of stochastic process is a Markov process, a stochastic process that has a limited form of “historical” precisely define this dependency, let \(\{ {X_t},t \in \mathcal{T}\}\) be a stochastic process defined on the parameter set \(\mathcal{T}\).We think of \(\mathcal{T} \subset {\text{[0,}}\infty {\text{)}}\) in terms of time, and the values that X t can.

The book offers an accessible reference for researchers in the probability, statistics and special functions communities. It gives a variety of interdisciplinary relations between the two main ingredients of stochastic processes and orthogonal polynomials.

Hermite Polynomials and Brownian motion. Ask Question Asked 5 years, 5 months ago. Active 3 years, 11 months ago. Viewed 2k times 7. 7 $\begingroup$ I am asked to prove the following: Browse other questions tagged stochastic-processes stochastic-calculus brownian-motion stochastic-integrals stochastic-analysis or ask your own question.

Orthogonal Polynomials 3 Orthogonality Relations 3 Three-Term Recurrence Relation 3 Classical Orthogonal Polynomials 4 Hypergeometric Type Equations 4 Classification of Classical Orthogonal Polynomials. 6 The Askey Scheme 10 2 Stochastic Processes 15 Markov Processes 15 Markov Chains Classical Orthogonal Polynomials of a Discrete Vari-able, Springer-Verlag, Berlin ().

[38] J. Ohkubo. On dualities for SSEP and ASEP with open boundary conditions. Journal of Physics A: Mathematical and Theoretical, (). [39] W. Schoutens. Stochastic Processes and Orthogonal Polynomials, Springer (). [40] G.M. Schu¨tz. Markov processes which are reversible with either Gamma, Normal, Poisson or Negative Binomial stationary distributions in the Meixner class and have orthogonal polynomial eigenfunctions are characterized as being processes subordinated to well-known diffusion processes for the Gamma and Normal, and birth and death processes for the Poisson and Negative by: Somehow I can't find the explicit definition of when two processes are supposed to be orthogonal or independent anywhere.

what are the metric and the space used to define orthogonality of stochastic processes. stochastic-processes probability theory. share A good reference for this topic is Protter's book. Can look up the exact section.

Contents of the Book 5 2 Stochastic Processes and Random Fields 7 Random Variables 7 Introduction 7 Operations with Random Variables 8 Random Vectors 12 Decomposition of Correlation Matrix 16 Stochastic Processes 17 Speciﬁcation of Stochastic Processes 17 Moment Functions of a Stochastic Process stochastic processes (birth and death processes; prediction theory), data sorting and compression, Radon transform and computer tomography.

This work is a. Polynomial chaos (PC), also called Wiener chaos expansion, is a non-sampling-based method to determine evolution of uncertainty in a dynamical system when there is probabilistic uncertainty in the system parameters. PC was first introduced by Norbert Wiener where Hermite polynomials were used to model stochastic processes with Gaussian random variables.

It can be thought of as an extension of. Stochastic processes with orthogonal polynomial eigenfunctions Article in Journal of Computational and Applied Mathematics (3) December with 41 Reads How we measure 'reads'Author: Bob Griffiths.

The stochastic input is represented spectrally by employing orthogonal polynomial functionals from the Askey scheme as the trial basis in the random space.

A standard Galerkin projection is applied in the random dimension to obtain the equations in the weak by: Orthogonal decompositions for generalized stochastic processes with independent values Eugene Lytvynov (Swansea University, UK) (Wrocław, 07 – ).

polynomial chaos, Askey scheme, orthogonal polynomials, stochastic differential equations, spectral methods, Galerkin projection.

SIAM Journal on Scientific ComputingDimension reduction of Karhunen-Loeve expansion for simulation of stochastic processes. Journal of Cited by: [email protected] first graduate-level textbook to focus on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC).

These fast, efficient, and accurate methods are an extension of the classical Price: $We present a new method for solving stochastic differential equations based on Galerkin projections and extensions of Wiener's polynomial chaos.

Specifically, we represent the stochastic processes Cited by: