5 edition of **Parameter estimation for stochastic processes** found in the catalog.

Parameter estimation for stochastic processes

Kutoyants, Yu. A.

- 44 Want to read
- 16 Currently reading

Published
**1984**
by Heldermann in Berlin
.

Written in English

- Stochastic processes.,
- Parameter estimation.

**Edition Notes**

Statement | Yu. A. Kutoyants ; translated and edited by B.L.S. Prakasa Rao. |

Series | Research and exposition in mathematics ;, 6 |

Classifications | |
---|---|

LC Classifications | QA274 .K88 1984 |

The Physical Object | |

Pagination | 206 p. ; |

Number of Pages | 206 |

ID Numbers | |

Open Library | OL2588712M |

ISBN 10 | 3885382067 |

LC Control Number | 85139642 |

In this work, three kinetic parameter estimation methods for stochastic models were developed based on two criteria: maximum likelihood (ML) and density function distance (DFD). Two scenarios of practical application were considered involving both sparsely and densely populated datasets (i.e. Cited by: Other useful references for numerical methods are [4,5,6,7]. Highly specialistic references for SDE theory and stochastic calculus are [8,9,10,11]; important references for parameter estimation of diffusion processes are [12,13]. See also the Toolbox User's Guide and references therein. Documentation Take a look at the pdf User's Guide (~ Mb.

Books shelved as stochastic-processes: Introduction to Stochastic Processes by Gregory F. Lawler, Adventures in Stochastic Processes by Sidney I. Resnick. Parameter estimation in engineering and science ML estimation model tj multiresponse nonlinear normal distribution Note obtained optimal experiment ordinary least squares parameter estimation parameter values parameter vector prior information probability Estadistica Mathematics / Probability & Statistics / Stochastic Processes.

Summary. Recursive Identification and Parameter Estimation describes a recursive approach to solving system identification and parameter estimation problems arising from diverse ing rigorous theoretical analysis, it presents the material and proposed algorithms in a manner that makes it easy to understand—providing readers with the modeling and identification skills required for. Read "Kutoyants, Yu. A.: Parameter Estimation for Stochastic Processes. Translated and edited by B. L. S. PRAKASA RAO. Heldermann Verlag, Berlin —, DM, Biometrical Journal" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at .

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Parameter estimation for stochastic processes. [Yu A Kutoyants] A translation of a completely revised and extended edition of the Russian book Estimating the parameters of random processes (Otsenivanie parametrov sluchainikh procesov) published by the Armenian Academy of Sciences, Yerevan ().

# Parameter estimation\/span> \u00A0. The Vere-Jones book on point processes as a bit more info, but not really fully fleshed examples. It seems a bit odd that there is not more on numerical methods and estimation of parameters for stochastic processes. Since there is so much theory on the numerical issues surrounding numerical estimation of say ordinary differential equations and.

Parameter Estimation in Stochastic Differential Equations. and a parameter estimation (Weber et al. Financial Regression and Organization. in the case o f stochastic processes we aim at a. Other chapters consider problems of prediction, filtering, and parameter estimation for some simple discrete-time linear stochastic processes.

This book discusses as well the ergodic type chains with finite and countable state-spaces and describes some results on.

The first new introduction to stochastic processes in 20 years incorporates a modern, innovative approach to estimation and control theory. Stochastic Processes, Estimation, and Control: The Entropy Approach provides a comprehensive, up-to-date introduction to stochastic processes, together with a concise review of probability and system by: 2.

Then, samples of the stochastic process are used as “surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. Attractive features of this two-step approach include modularity and trivial parallelizability. Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions.

The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes.

For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the. Parameter estimation for stochastic processes (Research and exposition in mathematics) Paperback – by Yu. A Kutoyants (Author) › Visit Amazon's Yu.

A Kutoyants Page. Find all the books, read about the author, and more. See search results for this author. Are you an author.

Cited by: Most of the research focuses on the parameter estimation of (model-specific) stochastic processes; in particular [33] is the pioneering work for the parameter estimation for a general stochastic.

This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian processes, martingales, Markov properties, continuity and related properties of trajectories with contemporary.

Probability - Random Variables and Stochastic Processes Probability, Random Variables And Stochastic Processes was designed for students who are pursuing senior or graduate level courses, in probability.

Those in the disciplines of mathematics phy. An efficient and flexible scheme for parameter estimation in stochastic grey-box models has been presented. Based on the extended Kalman filter it features maximum likelihood as well as maximum a posteriori estimation on multiple independent data sets, including irregularly sampled data sets and data sets with occasional outliers and missing Cited by: This importance class of stochastic estimation problems has ramifications for the estimation and control theory presented in the remainder of this book.

Minimum Variance Estimation. The thought may have crossed your mind that conditional expectation is an odd subject for a book chapter. Stochastic Processes, Estimation, and Control: The Entropy Approach is the first book to apply the thermodynamic principle of entropy to the measurement and analysis of uncertainty in systems.

Its new reformulation takes an important first step toward a unified approach to the theory of intelligent machines, where artificial intelligence and.

Estimation of Stochastic Processes is intended for researchers in the field of econometrics, financial mathematics, statistics or signal processing.

This book gives a deep understanding of spectral theory and estimation techniques Author: Erhan Cinlar. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning.

The book introduces students to the ideas and attitudes that underlie the statistical modeling of physical, chemical, biological systems. The text contains material the author have tried to convey to an audience composed mostly of graduate students. ( views) Probability, Statistics and Stochastic Processes by Cosma Rohilla Shalizi, From my point of view, a good introduction can be found in the book of S.

Resnick entitled "adventures in stochastic processes". It is well written, detailed and mix simple and elaborate remarks/exercises to satisfy a vast class of readers. Probab.

This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping.

Probability and Stochastic Processes. This book covers the following topics: Basic Concepts of Probability Theory, Random Variables, Multiple Random Variables, Vector Random Variables, Sums of Random Variables and Long-Term Averages, Random Processes, Analysis and Processing of Random Signals, Markov Chains, Introduction to Queueing Theory and Elements of a Queueing System.processes driven by a fBm for modeling a stochastic phenomena with possible long range dependence.

In a series of papers Prakasa Rao (, and ) discussed various methods of estimation of the parameter of interest, including maximum likelihood estimation, in such processes. An extensive review on most of the recent developmentsFile Size: KB.Description. This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes.

It combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian processes, martingales, Markov properties, continuity and related properties of trajectories with.