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Change-Point Analysis in Nonstationary Stochastic Models

2016-12-12 Mathematics
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Release : 2016-12-12
Genre : Mathematics
Kind : eBook
Book Rating : 955/5 ( reviews)

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Book Synopsis Change-Point Analysis in Nonstationary Stochastic Models by : Boris Brodsky

Download or read book Change-Point Analysis in Nonstationary Stochastic Models written by Boris Brodsky. This book was released on 2016-12-12. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the development of methods for detection and estimation of changes in complex systems. These systems are generally described by nonstationary stochastic models, which comprise both static and dynamic regimes, linear and nonlinear dynamics, and constant and time-variant structures of such systems. It covers both retrospective and sequential problems, particularly theoretical methods of optimal detection. Such methods are constructed and their characteristics are analyzed both theoretically and experimentally. Suitable for researchers working in change-point analysis and stochastic modelling, the book includes theoretical details combined with computer simulations and practical applications. Its rigorous approach will be appreciated by those looking to delve into the details of the methods, as well as those looking to apply them.

Stochastic Models, Statistics and Their Applications

2019-10-15 Mathematics
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Release : 2019-10-15
Genre : Mathematics
Kind : eBook
Book Rating : 657/5 ( reviews)

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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. This book was released on 2019-10-15. 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.

Bayesian Time Series Models

2011-08-11 Computers
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Release : 2011-08-11
Genre : Computers
Kind : eBook
Book Rating : 760/5 ( reviews)

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Book Synopsis Bayesian Time Series Models by : David Barber

Download or read book Bayesian Time Series Models written by David Barber. This book was released on 2011-08-11. 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.

Data Stream Mining & Processing

2020-11-04 Computers
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Release : 2020-11-04
Genre : Computers
Kind : eBook
Book Rating : 568/5 ( reviews)

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Book Synopsis Data Stream Mining & Processing by : Sergii Babichev

Download or read book Data Stream Mining & Processing written by Sergii Babichev. This book was released on 2020-11-04. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the third International Conference on Data Stream and Mining and Processing, DSMP 2020, held in Lviv, Ukraine*, in August 2020. The 36 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections of ​hybrid systems of computational intelligence; machine vision and pattern recognition; dynamic data mining & data stream mining; big data & data science using intelligent approaches. *The conference was held virtually due to the COVID-19 pandemic.

Building a Platform for Data-Driven Pandemic Prediction

2021-09-14 Medical
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Release : 2021-09-14
Genre : Medical
Kind : eBook
Book Rating : 222/5 ( reviews)

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Book Synopsis Building a Platform for Data-Driven Pandemic Prediction by : Dani Gamerman

Download or read book Building a Platform for Data-Driven Pandemic Prediction written by Dani Gamerman. This book was released on 2021-09-14. Available in PDF, EPUB and Kindle. Book excerpt: This book is about building platforms for pandemic prediction. It provides an overview of probabilistic prediction for pandemic modeling based on a data-driven approach. It also provides guidance on building platforms with currently available technology using tools such as R, Shiny, and interactive plotting programs. The focus is on the integration of statistics and computing tools rather than on an in-depth analysis of all possibilities on each side. Readers can follow different reading paths through the book, depending on their needs. The book is meant as a basis for further investigation of statistical modelling, implementation tools, monitoring aspects, and software functionalities. Features: A general but parsimonious class of models to perform statistical prediction for epidemics, using a Bayesian approach Implementation of automated routines to obtain daily prediction results How to interactively visualize the model results Strategies for monitoring the performance of the predictions and identifying potential issues in the results Discusses the many decisions required to develop and publish online platforms Supplemented by an R package and its specific functionalities to model epidemic outbreaks The book is geared towards practitioners with an interest in the development and presentation of results in an online platform of statistical analysis of epidemiological data. The primary audience includes applied statisticians, biostatisticians, computer scientists, epidemiologists, and professionals interested in learning more about epidemic modelling in general, including the COVID-19 pandemic, and platform building. The authors are professors at the Statistics Department at Universidade Federal de Minas Gerais. Their research records exhibit contributions applied to a number of areas of Science, including Epidemiology. Their research activities include books published with Chapman and Hall/CRC and papers in high quality journals. They have also been involved with academic management of graduate programs in Statistics and one of them is currently the President of the Brazilian Statistical Association.

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