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Bayesian Time Series Models

Bayesian Time Series Models
Author: David Barber
Publisher: Cambridge University Press
Total Pages: 432
Release: 2011-08-11
Genre: Computers
ISBN: 0521196760

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The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.


Sequential Analysis

Sequential Analysis
Author: Alexander Tartakovsky
Publisher: CRC Press
Total Pages: 600
Release: 2014-08-27
Genre: Mathematics
ISBN: 1439838216

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Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detecti


Parametric Statistical Change Point Analysis

Parametric Statistical Change Point Analysis
Author: Jie Chen
Publisher: Springer Science & Business Media
Total Pages: 190
Release: 2013-11-11
Genre: Mathematics
ISBN: 1475731310

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Recently there has been a keen interest in the statistical analysis of change point detec tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential.


Climate Time Series Analysis

Climate Time Series Analysis
Author: Manfred Mudelsee
Publisher: Springer Science & Business Media
Total Pages: 497
Release: 2010-08-26
Genre: Science
ISBN: 9048194822

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Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.


Limit Theorems in Change-Point Analysis

Limit Theorems in Change-Point Analysis
Author: Miklós Csörgö
Publisher: John Wiley & Sons
Total Pages: 448
Release: 1997-12-29
Genre: Mathematics
ISBN:

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Change-point problems arise in a variety of experimental and mathematical sciences, as well as in engineering and health sciences. This rigorously researched text provides a comprehensive review of recent probabilistic methods for detecting various types of possible changes in the distribution of chronologically ordered observations. Further developing the already well-established theory of weighted approximations and weak convergence, the authors provide a thorough survey of parametric and non-parametric methods, regression and time series models together with sequential methods. All but the most basic models are carefully developed with detailed proofs, and illustrated by using a number of data sets. Contains a thorough survey of: The Likelihood Approach Non-Parametric Methods Linear Models Dependent Observations This book is undoubtedly of interest to all probabilists and statisticians, experimental and health scientists, engineers, and essential for those working on quality control and surveillance problems. Foreword by David Kendall


Activity Learning

Activity Learning
Author: Diane J. Cook
Publisher: John Wiley & Sons
Total Pages: 288
Release: 2015-02-06
Genre: Technology & Engineering
ISBN: 1119010233

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Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.


Detection of Abrupt Changes

Detection of Abrupt Changes
Author: Michèle Basseville
Publisher:
Total Pages: 568
Release: 1993
Genre: Mathematics
ISBN:

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Presents mathematical tools and techniques for solving change detection problems in wide domains like signal processing, controlled systems and monitoring. The book covers a wide class of stochastic processes, including scalar independent observations and multidimensional dependent ARMA.


Forecasting: principles and practice

Forecasting: principles and practice
Author: Rob J Hyndman
Publisher: OTexts
Total Pages: 380
Release: 2018-05-08
Genre: Business & Economics
ISBN: 0987507117

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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.


Density Ratio Estimation in Machine Learning

Density Ratio Estimation in Machine Learning
Author: Masashi Sugiyama
Publisher: Cambridge University Press
Total Pages: 343
Release: 2012-02-20
Genre: Computers
ISBN: 0521190177

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This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.