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Group Psychotherapy with Addicted Populations An Integration of Theory and Practice

The Psychology of Addiction

Routledge Library Editions: Psychology of Education 53 Volume Set

Handbook of Discrete-Valued Time Series

The Subject of Addiction Psychoanalysis and The Administration of Enjoyment

Groundwater Economics Two-Volume Set

Deep Learning in Time Series Analysis

Deep Learning in Time Series Analysis

Deep learning is an important element of artificial intelligence especially in applications such as image classification in which various architectures of neural network e. g. convolutional neural networks have yielded reliable results. This book introduces deep learning for time series analysis particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures. Cyclic time series usually have special traits that can be employed for better classification performance. These are addressed in the book. Processing cyclic time series is also covered herein. An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning including students engineers researchers and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis. | Deep Learning in Time Series Analysis

GBP 115.00
1

Concise Introduction to Logic and Set Theory

Scriptwriting for Web Series Writing for the Digital Age

Medical Image Processing Advanced Fuzzy Set Theoretic Techniques

Medical Image Processing Advanced Fuzzy Set Theoretic Techniques

Medical image analysis using advanced fuzzy set theoretic techniques is an exciting and dynamic branch of image processing. Since the introduction of fuzzy set theory there has been an explosion of interest in advanced fuzzy set theories—such as intuitionistic fuzzy and Type II fuzzy set—that represent uncertainty in a better way. Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques deals with the application of intuitionistic fuzzy and Type II fuzzy set theories for medical image analysis. Designed for graduate and doctorate students this higher-level text:Provides a brief introduction to advanced fuzzy set theory fuzzy/intuitionistic fuzzy aggregation operators and distance/similarity measuresCovers medical image enhancement using advanced fuzzy sets including MATLAB®-based examples to increase contrast of the imagesDescribes intuitionistic fuzzy and Type II fuzzy thresholding techniques that separate different regions/leukocyte types/abnormal lesionsDemonstrates the clustering of unwanted lesions/regions even in the presence of noise by applying intuitionistic fuzzy clusteringHighlights the edges of poorly illuminated images and uses intuitionistic fuzzy edge detection to find the edges of different regionsDefines fuzzy mathematical morphology and explores its application using the Lukasiewicz operator t-norms and t-conormsMedical Image Processing: Advanced Fuzzy Set Theoretic Techniques is useful not only for students but also for teachers engineers scientists and those interested in the field of medical image analysis. A basic knowledge of fuzzy set is required along with a solid understanding of mathematics and image processing. | Medical Image Processing Advanced Fuzzy Set Theoretic Techniques

GBP 44.99
1

Routledge Library Editions: Psychiatry 24 Volume Set

Time Series Modelling with Unobserved Components

Time Series Modelling with Unobserved Components

Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis exponential smoothing and ARIMA the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical overview of the UCM approach covering some theoretical details several applications and the software for implementing UCMs. The book’s first part discusses introductory time series and prediction theory. Unlike most other books on time series this text includes a chapter on prediction at the beginning because the problem of predicting is not limited to the field of time series analysis. The second part introduces the UCM the state space form and related algorithms. It also provides practical modeling strategies to build and select the UCM that best fits the needs of time series analysts. The third part presents real-world applications with a chapter focusing on business cycle analysis and the construction of band-pass filters using UCMs. The book also reviews software packages that offer ready-to-use procedures for UCMs as well as systems popular among statisticians and econometricians that allow general estimation of models in state space form. This book demonstrates the numerous benefits of using UCMs to model time series data. UCMs are simple to specify their results are easy to visualize and communicate to non-specialists and their forecasting performance is competitive. Moreover various types of outliers can easily be identified missing values are effortlessly managed and working contemporaneously with time series observed at different frequencies poses no problem.

GBP 44.99
1

Time Series Modeling Computation and Inference Second Edition

Time Series Modeling Computation and Inference Second Edition

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference Time Series: Modeling Computation and Inference Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling analysis and forecasting a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis and contacts research frontiers in multivariate time series modeling and forecasting. It presents overviews of several classes of models and related methodology for inference statistical computation for model fitting and assessment and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields including signal processing biomedicine environmental science and finance. Along with core models and methods the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years and contacts recent and relevant modeling developments and research challenges. New in the second edition: Expanded on aspects of core model theory and methodology. Multiple new examples and exercises. Detailed development of dynamic factor models. Updated discussion and connections with recent and current research frontiers. | Time Series Modeling Computation and Inference Second Edition

GBP 44.99
1

Time Series A First Course with Bootstrap Starter

Time Series A First Course with Bootstrap Starter

Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness (ii) computational illustration and implementation and (iii) conciseness and accessibility to upper-level undergraduate and M. S. students. Basic theoretical results are presented in a mathematically convincing way and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results. The book provides the foundation of time series methods including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth as well as frequency domain methods. Entropy and other information theoretic notions are introduced with applications to time series modeling. The second half of the book focuses on statistical inference the fitting of time series models as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain the discussion of entropy maximization and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples as well as the solutions to exercises. | Time Series A First Course with Bootstrap Starter

GBP 38.99
1

Bringing Set and Costume Designs to Fruition Made by Teams

Level Set Method in Medical Imaging Segmentation

Nonlinear Time Series Semiparametric and Nonparametric Methods

Nonlinear Time Series Semiparametric and Nonparametric Methods

Useful in the theoretical and empirical analysis of nonlinear time series data semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date overview of the latest developments in the field Nonlinear Time Series: Semiparametric and Nonparametric Methods focuses on various semiparametric methods in model estimation specification testing and selection of time series data. After a brief introduction the book examines semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data. It also assesses some newly proposed semiparametric estimation procedures for time series data with long-range dependence. Even though the book only deals with climatological and financial data the estimation and specifications methods discussed can be applied to models with real-world data in many disciplines. This resource covers key methods in time series analysis and provides the necessary theoretical details. The latest applied finance and financial econometrics results and applications presented in the book enable researchers and graduate students to keep abreast of developments in the field. | Nonlinear Time Series Semiparametric and Nonparametric Methods

GBP 59.99
1

Applied Bayesian Forecasting and Time Series Analysis

Multilingual Fiction Series Genres Geographies and Performances

Multiplicative Differential Equations Two Volume Set

Multiplicative Differential Equations Two Volume Set

Multiplicative Differential Equations: Volume I is the first part of a comprehensive approach to the subject. It continues a series of books written by the authors on multiplicative geometric approaches to key mathematical topics. This volume begins with a basic introduction to multiplicative differential equations and then moves on to first and second order equations as well as the question of existence and unique of solutions. Each chapter ends with a section of practical problems. The book is accessible to graduate students and researchers in mathematics physics engineering and biology. Multiplicative Differential Equations: Volume 2 is the second part of a comprehensive approach to the subject. It continues a series of books written by the authors on multiplicative geometric approaches to key mathematical topics. This volume is devoted to the theory of multiplicative differential systems. The asymptotic behavior of the solutions of such systems is studied. Stability theory for multiplicative linear and nonlinear systems is introduced and boundary value problems for second order multiplicative linear and nonlinear equations are explored. The authors also present first order multiplicative partial differential equations. Each chapter ends with a section of practical problems. The book is accessible to graduate students and researchers in mathematics physics engineering and biology. | Multiplicative Differential Equations Two Volume Set

GBP 170.00
1

Contemporary British Television Crime Drama Cops on the Box

Addressing Special Needs and Disability in the Curriculum 11 Book Set