Author: Simone HermannPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Bayesian Prediction for a Jump Diffusion Process with Application to Crack Growth in Fatigue Experiments
Author: Simone HermannPattern Recognition: From Classical To Modern Approaches
Author: Sankar Kumar PalPublisher: World Scientific
ISBN: 9814490636
Category : Computers
Languages : en
Pages : 635
Book Description
This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource.
Statistical Inference for Fractional Diffusion Processes
Author: B. L. S. Prakasa RaoPublisher: John Wiley & Sons
ISBN: 0470975768
Category : Mathematics
Languages : en
Pages : 213
Book Description
Stochastic processes are widely used for model building in the social, physical, engineering and life sciences as well as in financial economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications point of view. This book deals with Fractional Diffusion Processes and statistical inference for such stochastic processes. The main focus of the book is to consider parametric and nonparametric inference problems for fractional diffusion processes when a complete path of the process over a finite interval is observable. Key features: Introduces self-similar processes, fractional Brownian motion and stochastic integration with respect to fractional Brownian motion. Provides a comprehensive review of statistical inference for processes driven by fractional Brownian motion for modelling long range dependence. Presents a study of parametric and nonparametric inference problems for the fractional diffusion process. Discusses the fractional Brownian sheet and infinite dimensional fractional Brownian motion. Includes recent results and developments in the area of statistical inference of fractional diffusion processes. Researchers and students working on the statistics of fractional diffusion processes and applied mathematicians and statisticians involved in stochastic process modelling will benefit from this book.
Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
Author: Xiaohong ChenPublisher: Springer Science & Business Media
ISBN: 1461416531
Category : Business & Economics
Languages : en
Pages : 582
Book Description
This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
Bayesian Statistics 9
Author: José M. BernardoPublisher: Oxford University Press
ISBN: 0199694583
Category : Mathematics
Languages : en
Pages : 717
Book Description
Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Bayesian Time Series Models
Author: David BarberPublisher: Cambridge University Press
ISBN: 0521196760
Category : Computers
Languages : en
Pages : 432
Book Description
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Bayesian Filtering and Smoothing
Author: Simo SärkkäPublisher: Cambridge University Press
ISBN: 110703065X
Category : Computers
Languages : en
Pages : 255
Book Description
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Bayesian Analysis of Stochastic Process Models
Author: David InsuaPublisher: John Wiley & Sons
ISBN: 0470744537
Category : Mathematics
Languages : en
Pages : 315
Book Description
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.
Monte Carlo Methods and Models in Finance and Insurance
Author: Ralf KornPublisher: CRC Press
ISBN: 1420076191
Category : Business & Economics
Languages : en
Pages : 485
Book Description
Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Rom
Probability, Statistics and Seismology
Author: Daryl J. DaleyPublisher:
ISBN:
Category : Probabilities
Languages : en
Pages : 316
Book Description