Tag: Stochastic

Free Download Stochastic Petri Nets: Modelling, Stability, Simulation by Peter J. Haas
English | PDF | 2002 | 523 Pages | ISBN : 0387954457 | 24.3 MB
Written by a leading researcher this book presents an introduction to Stochastic Petri Nets covering the modeling power of the proposed SPN model, the stability conditions and the simulation methods. Its unique and well-written approach provides a timely and important addition to the literature. Appeals to a wide range of researchers in engineering, computer science, mathematics and OR.

Free Download Quantum Theory and Its Stochastic Limit by Luigi Accardi , Igor Volovich , Yun Gang Lu
English | PDF | 2002 | 485 Pages | ISBN : 3540419284 | 33.6 MB
Nowadays it is becoming clearer and clearer that, in the description of natural phenomena, the triadic scheme – microseopie, mesoscopic, macroscopic – is only a rough approximation and that there are many levels of description, probably an infinite hierarchy, in which the specific properties of a given level express some kind of cumulative or collective behaviour of properties or sys tems corresponding to the lower levels. One of the most interesting challenges for contemporary natural sciences is the comprehension of the connections among these different levels of description of reality and the deduction of the laws of higher levels in this hierarchy from basic laws corresponding to lower levels. Since these cumulative or collective phenomena are, typically, nonlin ear effects, the transition from this general program to concrete scientific achievements requires the developement of techniques which allow physical information to be extracted from nonlinear quantum systems. Explicitly in tegrable examples of such systems are rare, and the most interesting physical phenomena are not captured by them. Even in the case of linear systems the fact that an explicit solution is formally available is often useless, since it is impossible to interpret interesting physical phenomena from it.

Free Download Limit Theorems for Stochastic Processes by Jean Jacod , Albert N. Shiryaev
English | PDF | 2003 | 682 Pages | ISBN : 3540439323 | 54.8 MB
Initially the theory of convergence in law of stochastic processes was developed quite independently from the theory of martingales, semimartingales and stochastic integrals. Apart from a few exceptions essentially concerning diffusion processes, it is only recently that the relation between the two theories has been thoroughly studied. The authors of this Grundlehren volume, two of the international leaders in the field, propose a systematic exposition of convergence in law for stochastic processes, from the point of view of semimartingale theory, with emphasis on results that are useful for mathematical theory and mathematical statistics. This leads them to develop in detail some particularly useful parts of the general theory of stochastic processes, such as martingale problems, and absolute continuity or contiguity results. The book contains an introduction to the theory of martingales and semimartingales, random measures stochastic integrales, Skorokhod topology, etc., as well as a large number of results which have never appeared in book form, and some entirely new results. The second edition contains some additions to the text and references. Some parts are completely rewritten.

Free Download Modeling Market Prices Using Stochastic Processes with Wolfram Language
Released 1/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Advanced | Genre: eLearning | Language: English + srt | Duration: 57m | Size: 157 MB
The Wolfram Language contains a complete collection of stochastic processes and statistical distributions that can be fitted to a wide array of market phenomena. This course illustrates this by explaining the modeling of stock prices, portfolios, index returns, bonds, option prices, exchange rates, and conditional risk using stochastic processes such as the ARCH process, vector-valued time series, the ARMA model, Chen’s model, the Ito process, and Merton jump diffusion. Learn how to access financial data from the Wolfram Knowledge base, smooth and transform data, build models for examining stock prices and returns, test different types of models, examine distribution patterns of prices and returns, and more.

Free Download Stochastic Calculus via Regularizations by Francesco Russo, Pierre Vallois
English | November 16, 2022 | ISBN: 303109445X | 669 pages | MOBI | 98 Mb
The book constitutes an introduction to stochastic calculus, stochastic differential equations and related topics such as Malliavin calculus. On the other hand it focuses on the techniques of stochastic integration and calculus via regularization initiated by the authors. The definitions relies on a smoothing procedure of the integrator process, they generalize the usual Itô and Stratonovich integrals for Brownian motion but the integrator could also not be a semimartingale and the integrand is allowed to be anticipating. The resulting calculus requires a simple formalism: nevertheless it entails pathwise techniques even though it takes into account randomness. It allows connecting different types of pathwise and non pathwise integrals such as Young, fractional, Skorohod integrals, enlargement of filtration and rough paths. The covariation, but also high order variations, play a fundamental role in the calculus via regularization, which can also be applied for irregular integrators. A large class of Gaussian processes, various generalizations of semimartingales such that Dirichlet and weak Dirichlet processes are revisited. Stochastic calculus via regularization has been successfully used in applications, for instance in robust finance and on modeling vortex filaments in turbulence. The book is addressed to PhD students and researchers in stochastic analysis and applications to various fields.

Free Download Stationary Stochastic Models: An Introduction (World Scientific Series On Probability Theory And Its Applications) by Riccardo Gatto
English | June 28, 2022 | ISBN: 9811251835 | 414 pages | MOBI | 53 Mb
This volume provides a unified mathematical introduction to stationary time series models and to continuous time stationary stochastic processes. The analysis of these stationary models is carried out in time domain and in frequency domain. It begins with a practical discussion on stationarity, by which practical methods for obtaining stationary data are described. The presented topics are illustrated by numerous examples. Readers will find the following covered in a comprehensive manner: Autoregressive and moving average time series. Important properties such as causality. Autocovariance function and the spectral distribution of these models. Practical topics of time series like filtering and prediction. Basic concepts and definitions on the theory of stochastic processes, such as Wiener measure and process. General types of stochastic processes such as Gaussian, selfsimilar, compound and shot noise processes. Gaussian white noise, Langevin equation and Ornstein-Uhlenbeck process. Important related themes such as mean square properties of stationary processes and mean square integration. Spectral decomposition and spectral theorem of continuous time stationary processes. This central concept is followed by the theory of linear filters and their differential equations. At the end, some selected topics such as stationary random fields, simulation of Gaussian stationary processes, time series for planar directions, large deviations approximations and results of information theory are presented. A detailed appendix containing complementary materials will assist the reader with many technical aspects of the book.

Free Download Stochastic Differential Equations: An Introduction with Applications, Third Edition by Bernt Øksendal
English | PDF | 1992 | 240 Pages | ISBN : 3540533354 | 12 MB
From the reviews to the first edition: Most of the literature about stochastic differential equations seems to place so much emphasis on rigor and completeness that it scares the nonexperts away. These notes are an attempt to approach the subject from the nonexpert point of view.: Not knowing anything … about a subject to start with, what would I like to know first of all. My answer would be: 1) In what situations does the subject arise ? 2) What are its essential features? 3) What are the applications and the connections to other fields?" The author, a lucid mind with a fine pedagocical instinct, has written a splendid text that achieves his aims set forward above. He starts out by stating six problems in the introduction in which stochastic differential equations play an essential role in the solution. Then, while developing stochastic calculus, he frequently returns to these problems and variants thereof and to many other problems to show how thetheory works and to motivate the next step in the theoretical development. Needless to say, he restricts himself to stochastic integration with respectto Brownian motion. He is not hesitant to give some basic results without proof in order to leave room for "some more basic applications"… It can be an ideal text for a graduate course, but it is also recommended to analysts (in particular, those working in differential equations and deterministic dynamical systems and control) who wish to learn quickly what stochastic differential equations are all about. From: Acta Scientiarum Mathematicarum, Tom 50, 3-4, 1986.

Free Download Numerical Approximations of Stochastic Maxwell Equations: via Structure-Preserving Algorithms by Chuchu Chen , Jialin Hong , Lihai Ji
English | PDF EPUB (True) | 2024 | 293 Pages | ISBN : 981996685X | 40.8 MB
The stochastic Maxwell equations play an essential role in many fields, including fluctuational electrodynamics, statistical radiophysics, integrated circuits, and stochastic inverse problems.

Free Download Deterministic and Stochastic Optimal Control by Wendell Fleming , Raymond Rishel
English | PDF | 1975 | 231 Pages | ISBN : 0387901558 | 19.8 MB
This book may be regarded as consisting of two parts. In Chapters I-IV we pre sent what we regard as essential topics in an introduction to deterministic optimal control theory. This material has been used by the authors for one semester graduate-level courses at Brown University and the University of Kentucky. The simplest problem in calculus of variations is taken as the point of departure, in Chapter I. Chapters II, III, and IV deal with necessary conditions for an opti mum, existence and regularity theorems for optimal controls, and the method of dynamic programming. The beginning reader may find it useful first to learn the main results, corollaries, and examples. These tend to be found in the earlier parts of each chapter. We have deliberately postponed some difficult technical proofs to later parts of these chapters. In the second part of the book we give an introduction to stochastic optimal control for Markov diffusion processes. Our treatment follows the dynamic pro gramming method, and depends on the intimate relationship between second order partial differential equations of parabolic type and stochastic differential equations. This relationship is reviewed in Chapter V, which may be read inde pendently of Chapters I-IV. Chapter VI is based to a considerable extent on the authors’ work in stochastic control since 1961. It also includes two other topics important for applications, namely, the solution to the stochastic linear regulator and the separation principle.

Free Download Saeed Ghahramani, "Fundamentals of Probability: with Stochastic Processes, Third Edition"
English | 2015 | ISBN: 1498755011 | PDF | pages: 618 | 7.2 mb
Fundamentals of Probability with Stochastic Processes, Third Edition teaches probability in a natural way through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. The author takes a mathematically rigorous approach while closely adhering to the historical development of probability. He includes more than 1500 routine and challenging exercises, historical remarks, and discussions of probability problems recently published in journals, such as Mathematics Magazine and American Mathematical Monthly.