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ISBN 13 : 9783943556438
Total Pages : pages
Book Rating : 4.33/5 ( download)
Download or read book Bayesian Inference for Discretely Observed Diffusion Processes written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Christiane Fuchs
Publisher : Springer Science & Business Media
ISBN 13 : 3642259693
Total Pages : 439 pages
Book Rating : 4.92/5 ( download)
Download or read book Inference for Diffusion Processes written by Christiane Fuchs and published by Springer Science & Business Media. This book was released on 2013-01-18 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diffusion processes are a promising instrument for realistically modelling the time-continuous evolution of phenomena not only in the natural sciences but also in finance and economics. Their mathematical theory, however, is challenging, and hence diffusion modelling is often carried out incorrectly, and the according statistical inference is considered almost exclusively by theoreticians. This book explains both topics in an illustrative way which also addresses practitioners. It provides a complete overview of the current state of research and presents important, novel insights. The theory is demonstrated using real data applications.
Author : Lyle D. Broemeling
Publisher : CRC Press
ISBN 13 : 1315303574
Total Pages : 373 pages
Book Rating : 4.74/5 ( download)
Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : David Insua
Publisher : John Wiley & Sons
ISBN 13 : 0470744537
Total Pages : 315 pages
Book Rating : 4.36/5 ( download)
Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-05-07 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Mostafa Bachar
Publisher : Springer
ISBN 13 : 3642321577
Total Pages : 216 pages
Book Rating : 4.73/5 ( download)
Download or read book Stochastic Biomathematical Models written by Mostafa Bachar and published by Springer. This book was released on 2012-10-19 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.
Author : David Barber
Publisher : Cambridge University Press
ISBN 13 : 0521196760
Total Pages : 432 pages
Book Rating : 4.65/5 ( download)
Download or read book Bayesian Time Series Models written by David Barber and published by Cambridge University Press. This book was released on 2011-08-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Author : Jaya P. N. Bishwal
Publisher : Springer
ISBN 13 : 3540744487
Total Pages : 268 pages
Book Rating : 4.81/5 ( download)
Download or read book Parameter Estimation in Stochastic Differential Equations written by Jaya P. N. Bishwal and published by Springer. This book was released on 2007-09-26 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Author : Yury A. Kutoyants
Publisher : Springer Science & Business Media
ISBN 13 : 144713866X
Total Pages : 493 pages
Book Rating : 4.62/5 ( download)
Download or read book Statistical Inference for Ergodic Diffusion Processes written by Yury A. Kutoyants and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.
Author : Jan de Gier
Publisher : Springer
ISBN 13 : 3030041611
Total Pages : 691 pages
Book Rating : 4.18/5 ( download)
Download or read book 2017 MATRIX Annals written by Jan de Gier and published by Springer. This book was released on 2019-03-13 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATRIX is Australia’s international and residential mathematical research institute. It facilitates new collaborations and mathematical advances through intensive residential research programs, each 1-4 weeks in duration. This book is a scientific record of the eight programs held at MATRIX in its second year, 2017: - Hypergeometric Motives and Calabi–Yau Differential Equations - Computational Inverse Problems - Integrability in Low-Dimensional Quantum Systems - Elliptic Partial Differential Equations of Second Order: Celebrating 40 Years of Gilbarg and Trudinger’s Book - Combinatorics, Statistical Mechanics, and Conformal Field Theory - Mathematics of Risk - Tutte Centenary Retreat - Geometric R-Matrices: from Geometry to Probability The articles are grouped into peer-reviewed contributions and other contributions. The peer-reviewed articles present original results or reviews on a topic related to the MATRIX program; the remaining contributions are predominantly lecture notes or short articles based on talks or activities at MATRIX.
Author : Radu Tunaru
Publisher : World Scientific
ISBN 13 : 9814663425
Total Pages : 384 pages
Book Rating : 4.27/5 ( download)
Download or read book Model Risk in Financial Markets written by Radu Tunaru and published by World Scientific. This book was released on 2015-06-08 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: The financial systems in most developed countries today build up a large amount of model risk on a daily basis. However, this is not particularly visible as the financial risk management agenda is still dominated by the subprime-liquidity crisis, the sovereign crises, and other major political events. Losses caused by model risk are hard to identify and even when they are internally identified, as such, they are most likely to be classified as normal losses due to market evolution. Model Risk in Financial Markets: From Financial Engineering to Risk Management seeks to change the current perspective on model innovation, implementation and validation. This book presents a wide perspective on model risk related to financial markets, running the gamut from financial engineering to risk management, from financial mathematics to financial statistics. It combines theory and practice, both the classical and modern concepts being introduced for financial modelling. Quantitative finance is a relatively new area of research and much has been written on various directions of research and industry applications. In this book the reader gradually learns to develop a critical view on the fundamental theories and new models being proposed. Contents:IntroductionFundamental RelationshipsModel Risk in Interest Rate ModellingArbitrage TheoryDerivatives Pricing Under UncertaintyPortfolio Selection Under UncertaintyProbability Pitfalls of Financial CalculusModel Risk in Risk Measures CalculationsParameter Estimation RiskComputational ProblemsPortfolio Selection Using Sharpe RatioBayesian Calibration for Low Frequency DataMCMC Estimation of Credit Risk MeasuresLast But Not Least. Can We Avoid the Next Big Systemic Financial Crisis?Notations for the Study of MLE for CIR Process Readership: Graduate students, researchers, practitioners, senior managers in financial institutions and hedge-funds, regulators and risk managers, who are keen to understand the pitfalls of financial modelling, and also those who are looking for a career in model validation, product control and risk management functions. Key Features:Some innovative results are presented for the first timeCovers a wide range of models, results and applications in financial markets to demonstrate that model risk is generally spreadKeywords:Model Risk;Risk Management;Financial Engineering;Financial Markets
