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.
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Swedish mathematician...
Harald Dutz
David C. Cramer
Harald Atmanspacher
Harald Jähner
Harald Hartl
John Cramer
Harald Renz
Michael M. Cramer
Harald A. Mieg
Harald Rabe
Harald Hempfling
Dieses fachbuch enth�lt die grundlagen f�r die erstellung von kausalit�tsgutachten auf dem gebiet der erkrankungen, verletzungen und �berlastungssch�den am bewegungsapparat.
Harald A. Mieg
Harald Haarmann
Ben Cramer
Harald Nahrstedt
Harald Bauder
Harald Ansen
Harald Haarmann
Harald Bodenschatz
David C. Cramer
Hans-Harald Sedlacek
Harald Maurer
The mind and brain are usually considered as one and the same nonlinear, complex dynamical system, in which information processing can be described with vector and tensor transformations and with attractors in multidimensional state spaces.
Harald Fischer-Tiné
Harald Huppertz
Harald Berger
Harald Wolter-Von Dem Knesebeck
Harald Huppertz
Harald Birgfeld
Hans-Harald Sedlacek
Harald A. Wiltsche
Harald Maurer
The mind and brain are usually considered as one and the same nonlinear, complex dynamical system, in which information processing can be described with vector and tensor transformations and with attractors in multidimensional state spaces.
Gail L. Cramer
Harald Fischer-Tiné
Harald Maurer
The mind and brain are usually considered as one and the same nonlinear, complex dynamical system, in which information processing can be described with vector and tensor transformations and with attractors in multidimensional state spaces.
Harald Genau
Harald Schwillus
Odd Harald Hauge
Harald Sander
Harald Horst
Harald Köpping Athanasopoulos
Allen Cramer
A first hand account of a young army rifleman witnessing combat and the horror of wwii while still finding camaraderie and goodness through his experiences.
Gail L. Cramer
Harald Ingholt
Harald Ingholt
Harald Stefan
Harald L. Heubaum
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
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.
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.
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.
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.
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.
Alan J. King
This book is about modeling stochastic programs - models solved by optimization technology, whose solutions perform well under uncertainty.
Jean-Dominique Deuschel
W. Sendler
Peter Olofsson
"this book provides a unique and balanced approach to probability, statistics, and stochastic processes.
Arvid Næss
Stochastic dynamics of marine structures is a text for students and reference for professionals on the basic theory and methods used for stochastic modeling and analysis of marine structures subjected to environmental loads.
Kandethody M. Ramachandran
The subject theory is important in finance, economics, investment strategies, health sciences, environment, industrial engineering, etc..
Mikhail Lifshits
Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables.
H. Körezlioglu
Archil Gulisashvili
This book offers sharp asymptotic formulas with error estimates for distribution densities of stock prices, option pricing functions and implied volatilities in stochastic volatility models.
M. R. Leadbetter
Classical extreme value theory-the asymptotic distributional theory for maxima of independent, identically distributed random variables-may be regarded as roughly half a century old, even though its roots reach further back into mathematical antiquity.
Salvatore Di Piazza
A. Bensoussan
Stochastic control by functional analysis methods.
I︠U︡. E. Gliklikh
The main aim of this book is to develop the methods of global analysis and of stochastic analysis such that their combination allows one to have a more or less common treatment for areas of mathematical physics that traditionally are considered as quite d.
Julius S. Bendat
A timely update of the classic book on the theory and application of random data analysisfirst published in 1971, "random data" served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications.
Kiyosi Itō
Barry L. Nelson
Tohru Katayama
An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts.
International Conference on Analytical and Stochastic Modelling Techniques and Applications (17th 2010 Cardiff, Wales)
Serguei G. Dobrovolski
Sergio Albeverio
Stochastic analysis is a field of mathematical research having numerous interactions with other domains of mathematics such as partial differential equations, riemannian path spaces, dynamical systems, optimization.
Atle Seierstad
This book contains an introduction to three topics in stochastic control: discrete time stochastic control, i.
Italy) International Conference on Stochastic Models of Manufacturing and Service Operations (7th 2009 Ostuni
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.
Alexandre Joel Chorin
An introduction to probability-based modeling, this book covers the stochastic tools used in physics, chemistry, engineering and the life sciences.
Vidyadhar G. Kulkarni
This practical text aims to enable students in engineering, business, operations research, public policy, and computer science to model and analyze stochastic systems.
Richard M. Feldman
Providing coverage of stachastic processes for undergraduate and postgraduate courses, this work includes an early chapter on computer simulation, which develops an understanding of markov chains and processes.