Probability, Statistics, and Stochastic Processes for Engineers and Scientists, Haghighi, Aliakbar Montazer , Wickramasinghe, Ind

Автор: Gallager
Название: Stochastic Processes
ISBN: 1107039754 ISBN-13(EAN): 9781107039759
Издательство: Cambridge Academ
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Цена: 74970.00 T
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Описание: This definitive textbook provides a solid introduction to stochastic processes, covering both theory and applications. It is written by one of the world`s leading information theorists, evolving over twenty years of graduate classroom teaching, and is accompanied by over 300 exercises, with online solutions for instructors.

Автор: Morgan
Название: Counterfactuals and Causal Inference
ISBN: 1107694167 ISBN-13(EAN): 9781107694163
Издательство: Cambridge Academ
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Цена: 38010.00 T
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Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.

Автор: Ivan Stanimirovic?
Название: Stochastic Processes and their Applications
ISBN: 1773613782 ISBN-13(EAN): 9781773613789
Издательство: Mare Nostrum (Eurospan)
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Цена: 164470.00 T
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Описание: Presents the theory of random variables along with some practical skills to analyse various stochastic dynamical systems in economics, engineering and other fields. Coverage includes the most appropriate process for modelling in particular situations arising in economics, engineering and other fields.

Автор: Rosenthal Jeffrey S
Название: First Look At Stochastic Processes, A
ISBN: 9811208972 ISBN-13(EAN): 9789811208973
Издательство: World Scientific Publishing
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Цена: 36960.00 T
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Описание:

This textbook introduces the theory of stochastic processes, that is, randomness which proceeds in time. Using concrete examples like repeated gambling and jumping frogs, it presents fundamental mathematical results through simple, clear, logical theorems and examples. It covers in detail such essential material as Markov chain recurrence criteria, the Markov chain convergence theorem, and optional stopping theorems for martingales. The final chapter provides a brief introduction to Brownian motion, Markov processes in continuous time and space, Poisson processes, and renewal theory.

Interspersed throughout are applications to such topics as gambler's ruin probabilities, random walks on graphs, sequence waiting times, branching processes, stock option pricing, and Markov Chain Monte Carlo (MCMC) algorithms.

The focus is always on making the theory as well-motivated and accessible as possible, to allow students and readers to learn this fascinating subject as easily and painlessly as possible.

Автор: Radek Erban, S. Jonathan Chapman
Название: Stochastic Modelling of Reaction–Diffusion Processes
ISBN: 1108498124 ISBN-13(EAN): 9781108498128
Издательство: Cambridge Academ
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Цена: 116160.00 T
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Описание: This practical introduction covers mathematical methods for the analysis of stochastic models and their biological applications. Based on courses taught at the University of Oxford, the book can be used for self-study or as a supporting text for advanced undergraduate or beginning graduate-level courses in applied mathematics.

Автор: Wio Horacio Sergio
Название: Path Integrals For Stochastic Processes: An Introduction
ISBN: 9814447994 ISBN-13(EAN): 9789814447997
Издательство: World Scientific Publishing
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Цена: 68640.00 T
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Описание: This book provides an introductory albeit solid presentation of path integration techniques as applied to the field of stochastic processes. The subject began with the work of Wiener during the 1920's, corresponding to a sum over random trajectories, anticipating by two decades Feynman's famous work on the path integral representation of quantum mechanics. However, the true trigger for the application of these techniques within nonequilibrium statistical mechanics and stochastic processes was the work of Onsager and Machlup in the early 1950's. The last quarter of the 20th century has witnessed a growing interest in this technique and its application in several branches of research, even outside physics (for instance, in economy).The aim of this book is to offer a brief but complete presentation of the path integral approach to stochastic processes. It could be used as an advanced textbook for graduate students and even ambitious undergraduates in physics. It describes how to apply these techniques for both Markov and non-Markov processes. The path expansion (or semiclassical approximation) is discussed and adapted to the stochastic context. Also, some examples of nonlinear transformations and some applications are discussed, as well as examples of rather unusual applications. An extensive bibliography is included. The book is detailed enough to capture the interest of the curious reader, and complete enough to provide a solid background to explore the research literature and start exploiting the learned material in real situations. remove

Автор: Reinhard Hopfner
Название: Asymptotic Statistics: With a View to Stochastic Processes
ISBN: 3110250241 ISBN-13(EAN): 9783110250244
Издательство: Walter de Gruyter
Цена: 49530.00 T
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Описание: This textbook is devoted to the general asymptotic theory of statistical experiments. Local asymptotics for statistical models in the sense of local asymptotic (mixed) normality or local asymptotic quadraticity make up the core of the book. Numerous examples deal with classical independent and identically distributed models and with stochastic processes. The book can be read in different ways, according to possibly different mathematical preferences of the reader. One reader may focus on the statistical theory, and thus on the chapters about Gaussian shift models, mixed normal and quadratic models, and on local asymptotics where the limit model is a Gaussian shift or a mixed normal or a quadratic experiment (LAN, LAMN, LAQ). Another reader may prefer an introduction to stochastic process models where given statistical results apply, and thus concentrate on subsections or chapters on likelihood ratio processes and some diffusion type models where LAN, LAMN or LAQ occurs. Finally, readers might put together both aspects. The book is suitable for graduate students starting to work in statistics of stochastic processes, as well as for researchers interested in a precise introduction to this area.

Автор: Haghighi, Aliakbar Montazer , Wickramasinghe, Ind
Название: Probability, Statistics, and Stochastic Processes for Engineers and Scientists
ISBN: 0815375905 ISBN-13(EAN): 9780815375906
Издательство: Taylor&Francis
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Цена: 158230.00 T
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Описание: Featuring recent advances in probability, statistics, and stochastic processes, this new textbook presents Probability and Statistics, and an introduction to Stochastic Processes.

Автор: Moulin Pierre
Название: Statistical Inference for Engineers and Data Scientists
ISBN: 1107185920 ISBN-13(EAN): 9781107185920
Издательство: Cambridge Academ
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Цена: 67590.00 T
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Описание: An up-to-date and mathematically accessible introduction to the tools needed to address modern inference problems in engineering and data science. Richly illustrated with examples and exercises connecting the theory with practice, it is the `go to` guide for students studying the topic, and an excellent reference for researchers and practitioners.

Автор: Joseph K. Blitzstein, Jessica Hwang
Название: Introduction to Probability, Second Edition
ISBN: 1138369918 ISBN-13(EAN): 9781138369917
Издательство: Taylor&Francis
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Цена: 74510.00 T
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Описание: Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Автор: Giorgos Michel
Название: Probability & Stochastic Processes: A Friendly Introduction for Electrical & Computer Engineers
ISBN: 1681174529 ISBN-13(EAN): 9781681174525
Издательство: Gazelle Book Services
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Цена: 230210.00 T
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Описание: In probability theory, a stochastic process, or often random process, is a collection of random variables representing the evolution of some system of random values over time. This is the probabilistic counterpart to a deterministic process (or deterministic system). Instead of describing a process which can only evolve in one way (as in the case, for example, of solutions of an ordinary differential equation), in a stochastic, or random process, there is some indeterminacy: even if the initial condition is known, there are several directions in which the process may evolve. Classic examples of the stochastic process are guessing the length of a queue at a stated time given the random distribution over time of a number of people or objects entering and leaving the queue and guessing the amount of water in a reservoir based on the random distribution of rainfall and water usage. Stochastic processes were first studied rigorously in the late 19th century to aid in understanding financial markets and Brownian motion. This book covers characterisation, structural properties, inference and control of stochastic processes. It is concerned with concepts and techniques, and is oriented towards a broad spectrum of mathematical, scientific and engineering interests.