Hidden Markov Models for Time Series

An Introduction Using R, Second Edition

Author: Walter Zucchini,Iain L. MacDonald,Roland Langrock

Publisher: CRC Press

ISBN: 1482253844

Category: Mathematics

Page: 370

View: 2905

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture–recapture data

Hidden Markov and Other Models for Discrete- valued Time Series

Author: Iain L. MacDonald,Walter Zucchini

Publisher: CRC Press

ISBN: 9780412558504

Category: Mathematics

Page: 256

View: 7175

Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.

The Synoptic Problem and Statistics

Author: Andris Abakuks

Publisher: CRC Press

ISBN: 1466572027

Category: Mathematics

Page: 215

View: 7735

See How to Use Statistics for New Testament Interpretation The Synoptic Problem and Statistics lays the foundations for a new area of interdisciplinary research that uses statistical techniques to investigate the synoptic problem in New Testament studies, which concerns the relationships between the Gospels of Matthew, Mark, and Luke. There are potential applications of the techniques to study other sets of similar documents. Explore Hidden Markov Models for Textual Data The book provides an introductory account of the synoptic problem and relevant theories, literature, and research at a level suitable for academic and professional statisticians. For those with no special interest in biblical studies or textual analysis, the book presents core statistical material on the use of hidden Markov models to analyze binary time series. Biblical scholars interested in the synoptic problem or in the use of statistical methods for textual analysis can omit the more technical/mathematical aspects of the book. The binary time series data sets and R code used are available on the author’s website.

Dynamic Process Methodology in the Social and Developmental Sciences

Author: Jaan Valsiner,Peter C. M. Molenaar,Maria C.D.P. Lyra,Nandita Chaudhary

Publisher: Springer Science & Business Media

ISBN: 9780387959221

Category: Psychology

Page: 668

View: 9565

All psychological processes—like biological and social ones—are dynamic. Phenomena of nature, society, and the human psyche are context bound, constantly changing, and variable. This feature of reality is often not recognized in the social sciences where we operate with averaged data and with homogeneous stereotypes, and consider our consistency to be the cornerstone of rational being. Yet we are all inconsistent in our actions within a day, or from, one day to the next, and much of such inconsistency is of positive value for our survival and development. Our inconsistent behaviors and thoughts may appear chaotic, yet there is generality within this highly variable dynamic. The task of scientific methodologies—qualitative and quantitative—is to find out what that generality is. It is the aim of this handbook to bring into one framework various directions of construction of methodology of the dynamic processes that exist in the social sciences at the beginning of the 21st century. This handbook is set up to bring together pertinent methodological scholarship from all over the world, and equally from the quantitative and qualitative orientations to methodology. In addition to consolidating the pertinent knowledge base for the purposes of its further growth, this book serves the major educational role of bringing practitioners—students, researchers, and professionals interested in applications—the state of the art know-how about how to think about extracting evidence from single cases, and about the formal mathematical-statistical tools to use for these purposes.

Bayesian Time Series Models

Author: David Barber,A. Taylan Cemgil,Silvia Chiappa

Publisher: Cambridge University Press

ISBN: 0521196760

Category: Computers

Page: 417

View: 4472

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

Artificial Evolution

7th International Conference, Evolution Artificielle, EA 2005, Revised Selected Papers

Author: El-ghazali Talbi,Pierre Liardet,Pierre Collet,Evelyne Lutton,Marc Schoenauer

Publisher: Springer

ISBN: 3540335900

Category: Computers

Page: 310

View: 7336

This book constitutes the thoroughly refereed post-proceedings of the 7th International Conference on Artificial Evolution, EA 2005, held in Lille, France, in October 2005. The 26 revised full papers presented were carefully reviewed and selected from 78 submissions. The papers cover all aspects of artificial evolution: genetic programming, machine learning, combinatorial optimization, co-evolution, self-assembling, artificial life and bioinformatics.

Latent Markov Models for Longitudinal Data

Author: Francesco Bartolucci,Alessio Farcomeni,Fulvia Pennoni

Publisher: CRC Press

ISBN: 1466583711

Category: Mathematics

Page: 252

View: 4093

Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

Extreme Value Methods with Applications to Finance

Author: Serguei Y. Novak

Publisher: CRC Press

ISBN: 1439835748

Category: Mathematics

Page: 399

View: 2923

Extreme value theory (EVT) deals with extreme (rare) events, which are sometimes reported as outliers. Certain textbooks encourage readers to remove outliers—in other words, to correct reality if it does not fit the model. Recognizing that any model is only an approximation of reality, statisticians are eager to extract information about unknown distribution making as few assumptions as possible. Extreme Value Methods with Applications to Finance concentrates on modern topics in EVT, such as processes of exceedances, compound Poisson approximation, Poisson cluster approximation, and nonparametric estimation methods. These topics have not been fully focused on in other books on extremes. In addition, the book covers: Extremes in samples of random size Methods of estimating extreme quantiles and tail probabilities Self-normalized sums of random variables Measures of market risk Along with examples from finance and insurance to illustrate the methods, Extreme Value Methods with Applications to Finance includes over 200 exercises, making it useful as a reference book, self-study tool, or comprehensive course text. A systematic background to a rapidly growing branch of modern Probability and Statistics: extreme value theory for stationary sequences of random variables.

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009

12th International Conference, London, UK, September 20-24, 2009, Proceedings

Author: Guang-Zhong Yang,David J. Hawkes,Daniel Rueckert

Publisher: Springer Science & Business Media

ISBN: 3642042678

Category: Computers

Page: 1037

View: 9154

The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009. Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The first volume includes 125 papers divided in topical sections on cardiovascular image guided intervention and robotics; surgical navigation and tissue interaction; intra-operative imaging and endoscopic navigation; motion modelling and image formation; image registration; modelling and segmentation; image segmentation and classification; segmentation and atlas based techniques; neuroimage analysis; surgical navigation and robotics; image registration; and neuroimage analysis: structure and function.

Encyclopedia of Statistical Sciences

Author: N.A

Publisher: John Wiley & Sons

ISBN: 9780471743750

Category: Mathematics

Page: 664

View: 831

With the publication of this update installment, the Encyclopedia of Statistical Sciences retains its position as the only cutting-edge reference of choice for those working in statistics, probability theory, biostatistics, quality control, and economics and in applications of statistical methods in sociology, engineering, computer and communication science, biomedicine, psychology, and many other areas.

Global Trends in Information Systems and Software Applications

4th International Conference, ObCom 2011, Vellore, TN, India, December 9-11, 2011, Part II. Proceedings

Author: P. Venkata Krishna,M. Rajasekhara Babu,Ezendu Ariwa

Publisher: Springer

ISBN: 364229216X

Category: Computers

Page: 817

View: 1431

This 2-Volume-Set, CCIS 0269-CCIS 0270, constitutes the refereed proceedings of the International Conference on Global Trends in Computing and Communication (CCIS 0269) and the International Conference on Global Trends in Information Systems and Software Applications (CCIS 0270), ObCom 2011, held in Vellore, India, in December 2011. The 173 full papers presented together with a keynote paper and invited papers were carefully reviewed and selected from 842 submissions. The conference addresses issues associated with computing, communication and information. Its aim is to increase exponentially the participants' awareness of the current and future direction in the domains and to create a platform between researchers, leading industry developers and end users to interrelate.

Hands-On Markov Models with Python

Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem

Author: Ankur Ankan,Abinash Panda

Publisher: Packt Publishing

ISBN: 9781788625449

Category:

Page: 178

View: 6540

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn Key Features Build a variety of Hidden Markov Models (HMM) Create and apply models to any sequence of data to analyze, predict, and extract valuable insights Use natural language processing (NLP) techniques and 2D-HMM model for image segmentation Book Description Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you'll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You'll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you'll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects. What you will learn Explore a balance of both theoretical and practical aspects of HMM Implement HMMs using different datasets in Python using different packages Understand multiple inference algorithms and how to select the right algorithm to resolve your problems Develop a Bayesian approach to inference in HMMs Implement HMMs in finance, natural language processing (NLP), and image processing Determine the most likely sequence of hidden states in an HMM using the Viterbi algorithm Who this book is for Hands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data. Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book