Bayesian Analysis of Stochastic Process Models

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Publisher : John Wiley & Sons
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ISBN 10 : 9780470744536
Pages : 332 pages
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Rating : 4.4/5 (744 users download)


Download Bayesian Analysis of Stochastic Process Models by David Insua PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Analysis of Stochastic Process Models by David Insua. This book is available in ePub and PDF format with a single click unlimited downloads. Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.


Bayesian Inference for Stochastic Processes

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Publisher : CRC Press
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ISBN 10 : 9781315303581
Pages : 432 pages
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Rating : 4.0/5 (33 users download)


Download Bayesian Inference for Stochastic Processes by Lyle D. Broemeling PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Inference for Stochastic Processes by Lyle D. Broemeling. This book is available in ePub and PDF format with a single click unlimited downloads. This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.


Bayesian Analysis of Non-gaussian Stochastic Processes for Temporal and Spatial Data

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ISBN 10 : OCLC:903034851
Pages : pages
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Download Bayesian Analysis of Non-gaussian Stochastic Processes for Temporal and Spatial Data by Jiangyong Yin PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Analysis of Non-gaussian Stochastic Processes for Temporal and Spatial Data by Jiangyong Yin. This book is available in ePub and PDF format with a single click unlimited downloads. Gaussian stochastic process is the most commonly used approach for modeling time series and geo-statistical data. The Gaussianity assumption, however, is known to be insufficient or inappropriate in many problems. In this dissertation, I develop specific non-Gaussian models to capture the asymmetry and heavy tails of many real-world data indexed in the time, space or space-time domain.


Bayesian Inference for Stochastic Processes

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Publisher : CRC Press
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ISBN 10 : 9781315303574
Pages : 432 pages
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Rating : 4.0/5 (33 users download)


Download Bayesian Inference for Stochastic Processes by Lyle D. Broemeling PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Inference for Stochastic Processes by Lyle D. Broemeling. This book is available in ePub and PDF format with a single click unlimited downloads. This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.


Recent Advances In Stochastic Modeling And Data Analysis

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Publisher : World Scientific
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ISBN 10 : 9789814474474
Pages : 668 pages
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Rating : 4.7/5 (474 users download)


Download Recent Advances In Stochastic Modeling And Data Analysis by Christos H Skiadas PDF/Ebook Free clicking on the below button will initiate the downloading process of Recent Advances In Stochastic Modeling And Data Analysis by Christos H Skiadas. This book is available in ePub and PDF format with a single click unlimited downloads. This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues, survival analysis, and time series. The volume presents new results with potential for solving real-life problems and provides novel methods for solving these problems by analyzing the relevant data. The use of recent advances in different fields is emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural computing, and bioinformatics.


Statistical Methods for Survival Data Analysis

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Publisher : Wiley-Interscience
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ISBN 10 : UOM:39015056677324
Pages : 513 pages
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Download Statistical Methods for Survival Data Analysis by Elisa T. Lee PDF/Ebook Free clicking on the below button will initiate the downloading process of Statistical Methods for Survival Data Analysis by Elisa T. Lee. This book is available in ePub and PDF format with a single click unlimited downloads. Third Edition brings the text up to date with new material and updated references. New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model. Coverage of graphical methods has been deleted. Large data sets are provided on an FTP site for readers' convenience. Bibliographic remarks conclude each chapter.


Journal of the American Statistical Association

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ISBN 10 : UOM:39015072641353
Pages : pages
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Download Journal of the American Statistical Association by PDF/Ebook Free clicking on the below button will initiate the downloading process of Journal of the American Statistical Association by . This book is available in ePub and PDF format with a single click unlimited downloads.


Statistical Modeling and Analysis for Complex Data Problems

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Publisher : Springer Science & Business Media
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ISBN 10 : 0387245545
Pages : 323 pages
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Rating : 4.8/5 (387 users download)


Download Statistical Modeling and Analysis for Complex Data Problems by Pierre Duchesne PDF/Ebook Free clicking on the below button will initiate the downloading process of Statistical Modeling and Analysis for Complex Data Problems by Pierre Duchesne. This book is available in ePub and PDF format with a single click unlimited downloads. STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.


Bayesian Analysis of Stochastic Betas

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ISBN 10 : OCLC:1290347513
Pages : 49 pages
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Download Bayesian Analysis of Stochastic Betas by Gergana Jostova PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Analysis of Stochastic Betas by Gergana Jostova. This book is available in ePub and PDF format with a single click unlimited downloads. We propose a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates than GARCH betas, betas conditioned on aggregate or firm-level variables, and rolling-regression betas, even when the true betas are generated based on these competing specifications. Our model significantly improves out-of-sample hedging effectiveness. In asset-pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models and helps resolve asset-pricing anomalies such as the size, book-to-market, and idiosyncratic volatility effects in the cross-section of stock returns.


Bayesian Analysis of Stochastic and Deterministic Processes in the Error Correction Model

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ISBN 10 : OCLC:248151068
Pages : 60 pages
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Download Bayesian Analysis of Stochastic and Deterministic Processes in the Error Correction Model by Rodney W. Strachan PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Analysis of Stochastic and Deterministic Processes in the Error Correction Model by Rodney W. Strachan. This book is available in ePub and PDF format with a single click unlimited downloads.


Stochastic Modelling for Systems Biology, Third Edition

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Publisher : CRC Press
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ISBN 10 : 9781351000895
Pages : 384 pages
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Rating : 4.0/5 ( users download)


Download Stochastic Modelling for Systems Biology, Third Edition by Darren J. Wilkinson PDF/Ebook Free clicking on the below button will initiate the downloading process of Stochastic Modelling for Systems Biology, Third Edition by Darren J. Wilkinson. This book is available in ePub and PDF format with a single click unlimited downloads. Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.


Bayesian Models

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Publisher : Princeton University Press
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ISBN 10 : 9781400866557
Pages : 320 pages
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Rating : 4.6/5 (866 users download)


Download Bayesian Models by N. Thompson Hobbs PDF/Ebook Free clicking on the below button will initiate the downloading process of Bayesian Models by N. Thompson Hobbs. This book is available in ePub and PDF format with a single click unlimited downloads. Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models


The Oxford Handbook of Applied Bayesian Analysis

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Publisher : Oxford University Press
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ISBN 10 : 9780199548903
Pages : 889 pages
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Rating : 4.4/5 (548 users download)


Download The Oxford Handbook of Applied Bayesian Analysis by Anthony O' Hagan PDF/Ebook Free clicking on the below button will initiate the downloading process of The Oxford Handbook of Applied Bayesian Analysis by Anthony O' Hagan. This book is available in ePub and PDF format with a single click unlimited downloads. Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems.


Modern Statistical and Mathematical Methods in Reliability

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Publisher : World Scientific
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ISBN 10 : 9789812563569
Pages : 409 pages
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Rating : 4.6/5 (563 users download)


Download Modern Statistical and Mathematical Methods in Reliability by Alyson G. Wilson PDF/Ebook Free clicking on the below button will initiate the downloading process of Modern Statistical and Mathematical Methods in Reliability by Alyson G. Wilson. This book is available in ePub and PDF format with a single click unlimited downloads. This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico, June 21-25, 2004, the leading conference in reliability research. A broad overview of current research activities in reliability theory and its applications is provided with coverage on reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford, Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves.


Statistical and Computational Issues in Probability Modeling

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ISBN 10 : PSU:000013056339
Pages : 643 pages
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Download Statistical and Computational Issues in Probability Modeling by PDF/Ebook Free clicking on the below button will initiate the downloading process of Statistical and Computational Issues in Probability Modeling by . This book is available in ePub and PDF format with a single click unlimited downloads.


Uncertainty Analysis with High Dimensional Dependence Modelling

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Publisher : Wiley-Blackwell
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ISBN 10 : UOM:39015063338555
Pages : 284 pages
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Rating : 4./5 ( users download)


Download Uncertainty Analysis with High Dimensional Dependence Modelling by Dorota Kurowicka PDF/Ebook Free clicking on the below button will initiate the downloading process of Uncertainty Analysis with High Dimensional Dependence Modelling by Dorota Kurowicka. This book is available in ePub and PDF format with a single click unlimited downloads. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: an up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis; all the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion; numerous worked examples and applications; workbook problems, enabling use for teaching; software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors; and a website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples.".


Journal of Economic Literature

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ISBN 10 : UCSD:31822032768913
Pages : pages
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Download Journal of Economic Literature by PDF/Ebook Free clicking on the below button will initiate the downloading process of Journal of Economic Literature by . This book is available in ePub and PDF format with a single click unlimited downloads.


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