Developed from the author's course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial diffe.
Emmanuel Acho
Emmanuel Iduma
Emmanuel Karagiannis
Emmanuel Iduma
Peter Emmanuel Cookey
Pastor Omojevwe Brown Emmanuel
Christophe Emmanuel Premat
Emmanuel Durand
Emmanuel C. Ogu
Emmanuel Koukios
Emmanuel Carrère
Emmanuel Kolawole Oke
Ayodeji Emmanuel Oke
Emmanuel Guematcha
Emmanuel Schmitt
Pastor Omojevwe Brown Emmanuel
Emmanuel Acho
Emmanuel Iduma
Niyifitamahoro Emmanuel
Peter Emmanuel Cookey
Emmanuel DESTENAY
Emmanuel Lazega
Emmanuel Kelechi Egbugara
Emmanuel G. Reynaud
Destined to set the standard, the first book dealing exclusively with this revolutionary and novel imaging technology serves as an easy-to- understand introduction while offering numerous tips and tricks.
Emmanuel Kolawole Oke
Emmanuel Clarke
Emmanuel Agormeda
Emmanuel Taieb
Emmanuel Carrère
Emmanuel Isang
Emmanuel Carrère
Emmanuel Carrère
Emmanuel Elize Norestin
Ayodeji Emmanuel Oke
Pastor Omojevwe Brown Emmanuel
Emmanuel Katongole
Emmanuel Cooper
Emmanuel Ngara
Emmanuel Droit
Emmanuel Do Linh San
This book aims at filling a gap in the scientific literature by showing the important roles of, and some of the latest knowledge acquired on, the world's small carnivores.
Emmanuel Kingsford Owusu
This book presents an extensive study on the extant constructs of corruption in infrastructure-related projects and aims to contribute to the determination and elimination of its incidence and prevalence in infrastructure projects.
Emmanuel Manu
The use of secondary data for research can offer benefits, particularly when limited resources are available for conducting research using primary methods.
Emmanuel Craig
Emmanuel Akan Okon
Emmanuel Akan Okon
Jorge Emmanuel Torres Nilo
Emmanuel Acho
In uncomfortable conversations with a black man, acho takes on all the questions, large and small, insensitive and taboo, many white americans are afraid to ask--yet which all americans need the answers to, now more than ever.
Ambe Emmanuel Cheo
Emmanuel Wallace
Emmanuel-Pierre Guittet
Emmanuel Wallace
Emmanuel Gonzalez-Escobar
Emmanuel Flesch
Emmanuel C. Ogu
Kurt Langfeld
Stochastic phenomena play a central role in various scientific disciplines and underpin applications in popular industrial sectors.
Leonard Bolc
Originally published in 1995 time and logic examines understanding and application of temporal logic, presented in computational terms.
Edward P. C. Kao
Rainer Buckdahn
Sergei Silvestrov
Michael Damron
Lecture notes prepared for a course, january 2-3, 2017, atlanta, georgia..
Peter Guttorp
Stochastic modeling of scientific data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, markov random fields and hidden markov models in a clear, thoughtful and succinct manne.
Lifeng Ma
In this book, control and filtering problems for several classes of stochastic networked systems are discussed.
Peter Guttorp
Stochastic modeling of scientific data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, markov random fields and hidden markov models in a clear, thoughtful and succinct manne.
Yuliya Mishura
This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes.
Peter Watts Jones
Harald Cramér
Professor cramer, author of the pivotal mathematical methods of statistics (1946), examines problems in the theory of stochastic processes that can be considered as generalizations of problems in the classical theory of statistical inference.
Lyle D. Broemeling
This is the first book designed to introduce bayesian inference procedures for stochastic processes.
Lyle D. Broemeling
This is the first book designed to introduce bayesian inference procedures for stochastic processes.
Lyle D. Broemeling
This is the first book designed to introduce bayesian inference procedures for stochastic processes.
Karl K. Sabelfeld
This monograph is devoted to random walk based stochastic algorithms for solving high-dimensional boundary value problems of mathematical physics and chemistry.
Ramsés H. Mena
Douglas Kennedy
Ines M Del Puerto
Richard Durrett
Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (ms and phd students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory.
Dariusz Buraczewski
In this monograph the authors give a systematic approach to the probabilistic properties of the fixed point equation x=ax+b.
Vidyadhar G. Kulkarni
Building on the author's more than 35 years of teaching experience, modeling and analysis of stochastic systems, third edition, covers the most important classes of stochastic processes used in the modeling of diverse systems.
Emmanuel Gobet
Developed from the author's course at the ecole polytechnique, monte-carlo methods and stochastic processes: from linear to non-linear focuses on the simulation of stochastic processes in continuous time and their link with partial diffe.
David F. Anderson
This book focuses on counting processes and continuous-time markov chains motivated by examples and applications drawn from chemical networks in systems biology.
Nicolas Bouleau
A simplified approach to malliavin calculus adapted to poisson random measures is developed and applied in this book.
Pierre Devolder
This book presents basic stochastic processes, stochastic calculus including levy processes on one hand, and markov and semi markov models on the other.
Jinqiao Duan
The mathematical theory of stochastic dynamics has become an important tool in the modeling of uncertainty in many complex biological, physical, and chemical systems and in engineering applications - for example, gene regulation systems, neuronal networks.
Samuel N. Cohen
Richard Durrett
This test is designed for a master's level course in stochastic processes.
Georg Pflug
Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas.
Anne Remke
Paul H. Bezandry
This book lays the foundations for a theory on almost periodic stochastic processes and their applications to various stochastic differential equations, functional differential equations with delay, partial differential equations, and difference equations.
Vigirdas Mackevicius
This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide audience, from students of mathematics to practitioners in biology, chemistry, physics, and finances.
Québec) Séminaire de Mathématiques Supérieures (50th 2011 Montréal
Horacio Sergio Wio
Sidney I. Resnick
Stochastic processes are necessary ingredients for building models of a wide variety of phenomena exhibiting time varying randomness.
Aniello Amendola
Marius Iosifescu
This book provides a pedagogical examination of the way in which stochastic models are encountered in applied sciences and techniques such as physics, engineering, biology and genetics, economics and social sciences.
Michał Kisielewicz
this book aims to further develop the theory of stochastic functional inclusions and their applications for describing the solutions of the initial and boundary value problems for partial differential inclusions.
Paul E. Smith
There are essentially two theories of solutions that can be considered exact: the mcmillan-mayer theory and fluctuation solution theory (fst).
Robert G. Gallager
Jean-Claude Bertein
Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals.
Sidney I. Resnick
Stochastic processes are necessary ingredients for building models of a wide variety of phenomena exhibiting time varying randomness.
Wolfgang Paul
Hyeong Soo Chang
Markov decision process (mdp) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.
Gilles Zumbach
Most financial and investment decisions are based on considerations of possible future changes and require forecasts on the evolution of the financial world.
Jerome L. Stein
Stochastic optimal control (soe["a mathematical theory concerned with minimizing a cost (or maximizing a payout) pertaining to a controlled dynamic processunder uncertainty["has proven incredibly helpful to understanding and predicting debt crises and eva.
Sever Silvestru Dragomir
Inequalities of ostrowski and trapezoidal type for functions of selfadjoint operators on hilbert spaces presents recent results concerning ostrowski and trapezoidal type inequalities for continuous functions of bounded selfadjoint operators on complex hil.
Bo'az Klartag
Marek Capiński
This book focuses specifically on the key results in stochastic processes that have become essential for finance practitioners to understand.
Ivan Nourdin
This book explores several aspects of fractional brownian motion, including the stochastic integration, the study of its supremum and its appearance as limit of partial sums involving stationary sequences..
Huaizhong Zhao
Alan J. King
This book is about modeling stochastic programs - models solved by optimization technology, whose solutions perform well under uncertainty.