Stochastic Models Statistics And Their Applications

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Stochastic Models, Statistics and Their Applications

Author : Ansgar Steland
Publisher : Springer
ISBN 13 : 3319138812
Total Pages : 492 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Stochastic Models, Statistics and Their Applications by : Ansgar Steland

Download or read book Stochastic Models, Statistics and Their Applications written by Ansgar Steland and published by Springer. This book was released on 2015-02-04 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

Stochastic Models, Statistics and Their Applications

Author : Ansgar Steland
Publisher : Springer Nature
ISBN 13 : 3030286657
Total Pages : 450 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Stochastic Models, Statistics and Their Applications by : Ansgar Steland

Download or read book Stochastic Models, Statistics and Their Applications written by Ansgar Steland and published by Springer Nature. This book was released on 2019-10-15 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.

Statistical Topics and Stochastic Models for Dependent Data with Applications

Author : Vlad Stefan Barbu
Publisher : John Wiley & Sons
ISBN 13 : 1786306034
Total Pages : 288 pages
Book Rating : 4.7/5 (863 download)

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Book Synopsis Statistical Topics and Stochastic Models for Dependent Data with Applications by : Vlad Stefan Barbu

Download or read book Statistical Topics and Stochastic Models for Dependent Data with Applications written by Vlad Stefan Barbu and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.

Stochastic Modelling of Social Processes

Author : Andreas Diekmann
Publisher : Academic Press
ISBN 13 : 1483266567
Total Pages : 352 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Stochastic Modelling of Social Processes by : Andreas Diekmann

Download or read book Stochastic Modelling of Social Processes written by Andreas Diekmann and published by Academic Press. This book was released on 2014-05-10 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Modelling of Social Processes provides information pertinent to the development in the field of stochastic modeling and its applications in the social sciences. This book demonstrates that stochastic models can fulfill the goals of explanation and prediction. Organized into nine chapters, this book begins with an overview of stochastic models that fulfill normative, predictive, and structural–analytic roles with the aid of the theory of probability. This text then examines the study of labor market structures using analysis of job and career mobility, which is one of the approaches taken by sociologists in research on the labor market. Other chapters consider the characteristic trends and patterns from data on divorces. This book discusses as well the two approaches of stochastic modeling of social processes, namely competing risk models and semi-Markov processes. The final chapter deals with the practical application of regression models of survival data. This book is a valuable resource for social scientists and statisticians.

Stochastic Models: Analysis and Applications

Author : B. R. Bhat
Publisher : New Age International
ISBN 13 : 9788122412284
Total Pages : 412 pages
Book Rating : 4.4/5 (122 download)

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Book Synopsis Stochastic Models: Analysis and Applications by : B. R. Bhat

Download or read book Stochastic Models: Analysis and Applications written by B. R. Bhat and published by New Age International. This book was released on 2004 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Book Presents A Systematic Exposition Of The Basic Theory And Applications Of Stochastic Models.Emphasising The Modelling Rather Than Mathematical Aspects Of Stochastic Processes, The Book Bridges The Gap Between The Theory And Applications Of These Processes.The Basic Building Blocks Of Model Construction Are Explained In A Step By Step Manner, Starting From The Simplest Model Of Random Walk And Proceeding Gradually To More Complicated Models. Several Examples Are Given Throughout The Text To Illustrate Important Analytical Properties As Well As To Provide Applications.The Book Also Includes A Detailed Chapter On Inference For Stochastic Processes. This Chapter Highlights Some Of The Recent Developments In The Subject And Explains Them Through Illustrative Examples.An Important Feature Of The Book Is The Complements And Problems Section At The End Of Each Chapter Which Presents (I) Additional Properties Of The Model, (Ii) Extensions Of The Model, And (Iii) Applications Of The Model To Different Areas.With All These Features, This Is An Invaluable Text For Post-Graduate Students Of Statistics, Mathematics And Operation Research.

Stochastic Models, Statistics and Their Applications

Author :
Publisher :
ISBN 13 : 9783030286668
Total Pages : 449 pages
Book Rating : 4.2/5 (866 download)

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Book Synopsis Stochastic Models, Statistics and Their Applications by :

Download or read book Stochastic Models, Statistics and Their Applications written by and published by . This book was released on 2019 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.

Stochastic Modeling of Scientific Data

Author : Peter Guttorp
Publisher : CRC Press
ISBN 13 : 135141366X
Total Pages : 384 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Stochastic Modeling of Scientific Data by : Peter Guttorp

Download or read book Stochastic Modeling of Scientific Data written by Peter Guttorp and published by CRC Press. This book was released on 2018-03-29 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 manner. The distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analyzed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians. The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks. The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics.

An Introduction to Stochastic Modeling

Author : Howard M. Taylor
Publisher : Academic Press
ISBN 13 : 1483269272
Total Pages : 410 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis An Introduction to Stochastic Modeling by : Howard M. Taylor

Download or read book An Introduction to Stochastic Modeling written by Howard M. Taylor and published by Academic Press. This book was released on 2014-05-10 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Stochastic Modeling and Mathematical Statistics

Author : Francisco J. Samaniego
Publisher : CRC Press
ISBN 13 : 1466560479
Total Pages : 622 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Stochastic Modeling and Mathematical Statistics by : Francisco J. Samaniego

Download or read book Stochastic Modeling and Mathematical Statistics written by Francisco J. Samaniego and published by CRC Press. This book was released on 2014-01-14 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well

An Introduction to Stochastic Processes and Their Applications

Author : Petar Todorovic
Publisher : Springer Science & Business Media
ISBN 13 : 1461397421
Total Pages : 302 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis An Introduction to Stochastic Processes and Their Applications by : Petar Todorovic

Download or read book An Introduction to Stochastic Processes and Their Applications written by Petar Todorovic and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text on stochastic processes and their applications is based on a set of lectures given during the past several years at the University of California, Santa Barbara (UCSB). It is an introductory graduate course designed for classroom purposes. Its objective is to provide graduate students of statistics with an overview of some basic methods and techniques in the theory of stochastic processes. The only prerequisites are some rudiments of measure and integration theory and an intermediate course in probability theory. There are more than 50 examples and applications and 243 problems and complements which appear at the end of each chapter. The book consists of 10 chapters. Basic concepts and definitions are pro vided in Chapter 1. This chapter also contains a number of motivating ex amples and applications illustrating the practical use of the concepts. The last five sections are devoted to topics such as separability, continuity, and measurability of random processes, which are discussed in some detail. The concept of a simple point process on R+ is introduced in Chapter 2. Using the coupling inequality and Le Cam's lemma, it is shown that if its counting function is stochastically continuous and has independent increments, the point process is Poisson. When the counting function is Markovian, the sequence of arrival times is also a Markov process. Some related topics such as independent thinning and marked point processes are also discussed. In the final section, an application of these results to flood modeling is presented.

Constructive Computation in Stochastic Models with Applications

Author : Quan-Lin Li
Publisher : Springer Science & Business Media
ISBN 13 : 364211492X
Total Pages : 650 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Constructive Computation in Stochastic Models with Applications by : Quan-Lin Li

Download or read book Constructive Computation in Stochastic Models with Applications written by Quan-Lin Li and published by Springer Science & Business Media. This book was released on 2011-02-02 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Constructive Computation in Stochastic Models with Applications: The RG-Factorizations" provides a unified, constructive and algorithmic framework for numerical computation of many practical stochastic systems. It summarizes recent important advances in computational study of stochastic models from several crucial directions, such as stationary computation, transient solution, asymptotic analysis, reward processes, decision processes, sensitivity analysis as well as game theory. Graduate students, researchers and practicing engineers in the field of operations research, management sciences, applied probability, computer networks, manufacturing systems, transportation systems, insurance and finance, risk management and biological sciences will find this book valuable. Dr. Quan-Lin Li is an Associate Professor at the Department of Industrial Engineering of Tsinghua University, China.

Stochastic Epidemic Models and Their Statistical Analysis

Author : Hakan Andersson
Publisher : Springer Science & Business Media
ISBN 13 : 1461211581
Total Pages : 140 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Stochastic Epidemic Models and Their Statistical Analysis by : Hakan Andersson

Download or read book Stochastic Epidemic Models and Their Statistical Analysis written by Hakan Andersson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present lecture notes describe stochastic epidemic models and methods for their statistical analysis. Our aim is to present ideas for such models, and methods for their analysis; along the way we make practical use of several probabilistic and statistical techniques. This will be done without focusing on any specific disease, and instead rigorously analyzing rather simple models. The reader of these lecture notes could thus have a two-fold purpose in mind: to learn about epidemic models and their statistical analysis, and/or to learn and apply techniques in probability and statistics. The lecture notes require an early graduate level knowledge of probability and They introduce several techniques which might be new to students, but our statistics. intention is to present these keeping the technical level at a minlmum. Techniques that are explained and applied in the lecture notes are, for example: coupling, diffusion approximation, random graphs, likelihood theory for counting processes, martingales, the EM-algorithm and MCMC methods. The aim is to introduce and apply these techniques, thus hopefully motivating their further theoretical treatment. A few sections, mainly in Chapter 5, assume some knowledge of weak convergence; we hope that readers not familiar with this theory can understand the these parts at a heuristic level. The text is divided into two distinct but related parts: modelling and estimation.

Introduction to Modeling and Analysis of Stochastic Systems

Author : V. G. Kulkarni
Publisher : Springer
ISBN 13 : 1441917721
Total Pages : 313 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Introduction to Modeling and Analysis of Stochastic Systems by : V. G. Kulkarni

Download or read book Introduction to Modeling and Analysis of Stochastic Systems written by V. G. Kulkarni and published by Springer. This book was released on 2010-11-03 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.

Stochastic Models with Applications to Genetics, Cancers, AIDS and Other Biomedical Systems

Author : Wai-Yuan Tan
Publisher : World Scientific
ISBN 13 : 981448931X
Total Pages : 460 pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Stochastic Models with Applications to Genetics, Cancers, AIDS and Other Biomedical Systems by : Wai-Yuan Tan

Download or read book Stochastic Models with Applications to Genetics, Cancers, AIDS and Other Biomedical Systems written by Wai-Yuan Tan and published by World Scientific. This book was released on 2002-02-26 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a systematic treatment of Markov chains, diffusion processes and state space models, as well as alternative approaches to Markov chains through stochastic difference equations and stochastic differential equations. It illustrates how these processes and approaches are applied to many problems in genetics, carcinogenesis, AIDS epidemiology and other biomedical systems. One feature of the book is that it describes the basic MCMC (Markov chain and Monte Carlo) procedures and illustrates how to use the Gibbs sampling method and the multilevel Gibbs sampling method to solve many problems in genetics, carcinogenesis, AIDS and other biomedical systems. As another feature, the book develops many state space models for many genetic problems, carcinogenesis, AIDS epidemiology and HIV pathogenesis. It shows in detail how to use the multilevel Gibbs sampling method to estimate (or predict) simultaneously the state variables and the unknown parameters in cancer chemotherapy, carcinogenesis, AIDS epidemiology and HIV pathogenesis. As a matter of fact, this book is the first to develop many state space models for many genetic problems, carcinogenesis and other biomedical problems. Contents:Discrete Time Markov Chain Models in Genetics and Biomedical SystemsStationary Distributions and MCMC in Discrete Time Markov ChainsContinuous-Time Markov Chain Models in Genetics, Cancers and AIDSAbsorption Probabilities and Stationary Distributions in Continuous-Time Markov Chain ModelsDiffusion Models in Genetics, Cancer and AIDSAsymptotic Distributions, Stationary Distributions and Absorption Probabilities in Diffusion ModelsState Space Models and Some Examples from Cancer and AIDSSome General Theories of State Space Models and Applications Readership: Graduate students and researchers in probability & statistics and the life sciences. Keywords:Stochastic;Genetics;Cancers;AIDS;Biomedical SystemsReviews:“Its strengths include the large number of models described, many of which have previously been published only in research journals; its clear presentation of many detailed analyses; and good accounts of the biology behind the models.”Mathematical Reviews

Stochastic Models for Time Series

Author : Paul Doukhan
Publisher : Springer
ISBN 13 : 3319769383
Total Pages : 308 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Stochastic Models for Time Series by : Paul Doukhan

Download or read book Stochastic Models for Time Series written by Paul Doukhan and published by Springer. This book was released on 2018-04-17 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.

Stochastic Population Models

Author : James H. Matis
Publisher : Springer Science & Business Media
ISBN 13 : 1461212448
Total Pages : 215 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Stochastic Population Models by : James H. Matis

Download or read book Stochastic Population Models written by James H. Matis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on stochastic modeling of population processes. The book presents new symbolic mathematical software to develop practical methodological tools for stochastic population modeling. The book assumes calculus and some knowledge of mathematical modeling, including the use of differential equations and matrix algebra.

Stochastic Modeling

Author : Nicolas Lanchier
Publisher : Springer
ISBN 13 : 3319500384
Total Pages : 303 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Stochastic Modeling by : Nicolas Lanchier

Download or read book Stochastic Modeling written by Nicolas Lanchier and published by Springer. This book was released on 2017-01-27 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes. The first part of the text begins with a brief review of measure theory and revisits the main concepts of probability theory, from random variables to the standard limit theorems. The second part covers traditional material on stochastic processes, including martingales, discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding applications such as the gambler’s ruin chain, branching processes, symmetric random walks, and queueing systems. The third, more research-oriented part of the text, discusses special stochastic processes of interest in physics, biology, and sociology. Additional emphasis is placed on minimal models that have been used historically to develop new mathematical techniques in the field of stochastic processes: the logistic growth process, the Wright –Fisher model, Kingman’s coalescent, percolation models, the contact process, and the voter model. Further treatment of the material explains how these special processes are connected to each other from a modeling perspective as well as their simulation capabilities in C and MatlabTM.