Probability and statistics Books
Taylor & Francis Ltd Applied Microbiome Statistics
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£135.00
Taylor & Francis Ltd Applied Statistics in Social Sciences
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£104.50
Taylor & Francis Ltd Outstanding User Interfaces with Shiny
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£142.50
Taylor & Francis Ltd Dynamic Time Series Models using RINLA
Book SynopsisDynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework.The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series.Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-Trade Review"This book will interest current R-users with a background in time series analyses who would like to expand their knowledge regarding INLA and its application with R-INLA package. This book also provides illustrative examples which can contribute to the understanding of the applications of these methods. This book can also benefit academic researchers who would like to apply these types of approaches in their fields." Sébastien Bailly, French National Center for Medical Research (INSERM), France, ISCB, May 2023 Table of ContentsPreface. 1. Bayesian Analysis. 2. A Review of INLA. 3. Modeling Univariate Time Series. 4. More Topics on DLMs with R-INLA. 5. Modeling Time Series with Exogenous Predictors. 6. Structural Time Series Decomposition using R-INLA. 7. Hierarchical DLM. 8. INLA for Multivariate Dynamic Models. 9. Modeling Binary Time Series. 10. Modeling Count Time Series. 11. Modeling Stochastic Volatility. 12. Comparison of R-INLA to Other Bayesian Alternatives. 13. Resources for the User.
£80.74
Taylor & Francis Ltd Introduction to Statistical Methods for Financial Models
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£45.99
Taylor & Francis Ltd Probability and Statistical Inference
Book SynopsisPriced very competitively compared with other textbooks at this level!This gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inferencestudies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions develops notions of convergence in probability and distribution spotlights the central limit theorem (CLT) for the sample variance introduces sampling distributions and the Cornish-Fisher expansions concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity explaTrade Review"...the book contains unique features throughout. Examples are the moment problem, which is clarified through a nice example, the role of the probability generating functions, and the central limit theorem for the sample variance. Techniques and concepts are typically illustrated through a series of examples. Within a box is routinely summarized what it is that has been accomplished or where to go from that point. At the end of each chapter a long list of exercises is arranged according the sections. "---Zentralblatt fur Mathematik, 2000"…a marvelous book for students."-Statistical Papers "…a handy reference as well as a good textbook."-International Statistical Institute, Short Book Reviews Table of ContentsNotions of probability; expectations of functions of random variables; multivariate random variables; transformations and sampling distributions; notions of stochastic convergence; sufficiency, completeness and ancillarity; point estimation; tests of hypotheses; confidence interval estimation; Bayesian methods; likelihood ratio and other tests; large-sample inference; sample size determination - two-stage procedures. Appendices: abbreviations and notation; celebration of statistics - selected biographical notes; selected statistical tables.
£45.99
Taylor & Francis Ltd Javascript for R
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£47.49
Taylor & Francis Ltd Javascript for R
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£118.75
Taylor & Francis Renminbi Exchange Rate Forecasting Routledge Advances in Risk Management
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£128.25
Taylor & Francis Ltd An Introduction to IoT Analytics
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£44.99
Taylor & Francis Ltd ProjectBased R Companion to Introductory Statistics A ProjectBased Approach Using R
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£128.25
Taylor & Francis Ltd An Introduction to IoT Analytics
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£109.25
Taylor & Francis Ltd Explanatory Model Analysis
Book SynopsisExplanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model''s performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.Trade Review"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices.The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code.The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this.The rationale for the book is well-described.The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels."-Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful."-Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other."-Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular."-Ziv Shkedy, Data Science Institute, Hasselt University, Belgium'The book is clearly and consistently structured and well–written. The graphics are explained conceptually and mathematically. There are chapter sections on the pros and cons of what is proposed, where the authors are generally properly cautious and recommend a mixture of approaches.'- Antony Unwin, International Statistical Review, 2021 Volume 89, Issue 3"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices. The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code. The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this. The rationale for the book is well-described. The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels." -Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful." -Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other." -Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular." -Ziv Shkedy, Data Science Institute, Hasselt University, Belgium"We need to explore the models and learn about their behaviour. This book presents, explains, and summarises the techniques for doing so. Moreover, it provides code in R and Python for doing so. The methods have many similarities with those of sensitivity analysis developed within the Sensitivity Analysis of Model Output (SAMO) community. ... [M]any doctoral students, professional statisticians and researchers should ensure that they have access to it and know how to use its methods when dealing with highly complex functions in their data and model analysis." -Simon French, in the Journal of the Royal Statistics Society, Series A, June 2022"The book presents a valuable collection of methods for models’ exploration and diagnostics for various machine learning algorithms. It can be useful in the data and computer science courses for students and instructors, as well as for researchers and practitioners who need to analyze and interpret their statistical and machine learning models both of glass-box and blackbox kind. The book also serves as a great primary for applications of the R and Python software and their packages/libraries, so it is valuable in solving various problems of statistical prediction in various fields."-Stan Lipovetsky, in Technometrics, July 2022Table of ContentsI. Introduction 1. Introduction. 2. Model Development. 3. Do-it-yourself. 4. Datasets and models. II. Instance Level. 5. Introduction to Instance-level Exploration. 6. Break-down Plots for Additive Attributions. 7. Break-down Plots for Interactions. 8. Shapley Additive Explanations (SHAP) for Average Attributions. 9. Local Interpretable Model-agnostic Explanations (LIME). 10. Ceteris-paribus Profiles. 11. Ceteris-paribus Oscillations. 12. Local-diagnostics Plots. 13. Summary of Instance-level Exploration. III. Dataset Level. 14. Introduction to Dataset-level Exploration. 15. Model-performance Measures. 16. Variable-importance Measures. 17. Partial-dependence Profiles. 18. Local-dependence and Accumulated-dependence Profiles. 19. Residual Diagnostics Plots. 20. Summary of Model-level Exploration. IV. Use-cases. 21. FIFA 19.
£45.99
Taylor & Francis Ltd Renminbi Exchange Rate Forecasting
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£37.99
Taylor & Francis Ltd Random Matrices and NonCommutative Probability
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£142.50
Taylor & Francis Ltd Feynman Path Integrals in Quantum Mechanics and Statistical Physics
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£43.69
Taylor & Francis Candlestick Forecasting for Investments
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£128.25
Taylor & Francis Ltd Candlestick Forecasting for Investments
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£39.99
Taylor & Francis Ltd Random Matrices and NonCommutative Probability
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£54.14
Taylor & Francis Ltd Fundamentals of Causal Inference
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£76.42
Taylor & Francis Ltd Optimization of IntegerFractional Order Chaotic Systems by Metaheuristics and their Electronic Realization
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£51.99
Taylor & Francis Ltd Data Analytics Computational Statistics and Operations Research for Engineers
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£128.25
Taylor & Francis Ltd Advanced Survival Models
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£43.99
Taylor & Francis Ltd The Sequential Quadratic Hamiltonian Method
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£147.25
Taylor & Francis Ltd Introduction to HighDimensional Statistics
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£93.72
Taylor & Francis Ltd Data Mining and Exploration
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£128.25
Taylor & Francis Time Series Analysis of Discourse
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£21.99
Taylor & Francis Ltd Mathematical Theory of Bayesian Statistics
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£51.99
Taylor & Francis Ltd Design Analysis of Clinical Trials for Economic Evaluation Reimbursement
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£43.69
Taylor & Francis Ltd Missing Data Analysis in Practice
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£45.99
Taylor & Francis Ltd Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
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£43.69
Taylor & Francis Ltd Data Analysis with Competing Risks and Intermediate States
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£45.99
Taylor & Francis Ltd Sequential Analysis
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£43.69
Taylor & Francis Ltd Risk Measures and Insurance Solvency Benchmarks
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£114.00
Taylor & Francis Ltd Value of Information for Healthcare DecisionMaking
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£105.00
Taylor & Francis Ltd Structural Equation Modeling for Health and
Book SynopsisStructural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across many disciplines, addressing issues unique to health and medicine. SEM is often used in practice to model and test hypothesized causal relationships among observed and latent (unobserved) variables, including in analysis across time and groups. It can be viewed as the merging of a conceptual model, path diagram, confirmatory factor analysis, and path analysis. In this textbook the authors also discuss techniques, such as mixture modeling, that expand the capacity of SEM using a combination of both continuous and categorical latent variables. Features: Basic, intermediate, and advanced SEM topics Detailed applications, parTable of ContentsPart I Introduction to Concepts and Principles of Structural Equation Modeling for Health and Medical Research 1. Introduction and Brief History of Structural Equation Modeling for Health and Medical Research2. Vocabulary, Concepts and Usages of Structural Equation Modeling Part II Theory of Structural Equation Modeling 3 The Form of Structural Equation Models4 Model Estimation and Evaluation5 Model Identifiability and Equivalence Part III Applications and Examples of Structural Equation Modeling for Health and Medical Research 6 Choosing Among Competing Specifications 7 Measurement Models for Patient-Reported Outcomes and Other Health-related Outcomes8 Exploratory Factor Analysis9 Mediation and Moderation10 Measurement Bias, Multiple Indicator Multiple Cause Modeling and Multiple Group Modeling 11 Latent Class Analysis 12 Latent Profile Analysis13 Structural Equation Modeling with Longitudinal Data14 Growth Mixture Modeling 15 Special Topics
£43.99
Taylor & Francis Ltd Probability and Statistical Inference
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£45.99
Taylor & Francis Ltd Statistics and Data Visualisation with Python
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£114.00
Taylor & Francis Ltd Data Science for Engineers
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£111.89
Taylor & Francis Ltd Data Science and Machine Learning for NonProgrammers
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£93.72
Taylor & Francis Ltd PostShrinkage Strategies in Statistical and Machine Learning for High Dimensional Data
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£118.75
Taylor & Francis Ltd Fuzzy TOPSIS
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£147.25
Taylor & Francis Ltd Nanohertz Gravitational Wave Astronomy
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£56.04
Taylor & Francis Ltd HumanintheLoop
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£45.99
Taylor & Francis Ltd Testing R Code Chapman HallCRC the R
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£45.99
Taylor & Francis Ltd Reliability Analysis with Minitab
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£45.99
Taylor & Francis Ltd Artificial Intelligence and Causal Inference
Book SynopsisArtificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis mTrade Review" Both deep learning and causal inference are fast-moving fields, and the author covers the latest topics and methods well. The book has a high ratio of equations to text, and even more technical material is contained in appendices at the end of each chapter."Stanley E. Lazic, University of Ottawa, Series A: Statisics in Society, 2022."The book is suitable for use in a graduate-level course on AI. The exercises are challenging but their answers are provided in the end of the book. Not all contents are understandable by the statistics community or commonly useful in the practice of statistics. I enjoyed reading this book. I recommend this book to engineering, data science, predictive business, statistics and computing professionals."Ramalingam Shanmugam, School of Health Administration, Texas State University, San Marcos, Texas, Journal of Statistical Computation and Simulation, 2023.Table of Contents1. Deep Neural Networks. 2. Deep Wide Neural Networks. 3. Dynamics of Output of Neural Networks. 4. Deep Generative Models. 5. Representation Learning. 5. Graph Representation Learning. 6. Deep Learning for Causal Inference. 7. Deep Learning for Counterfactual Inference and Treatment Estimation. 8. Reinforcement Learning, Meta-Learning for Causal Inference and Quantum Causal Analysis.
£104.50
Taylor & Francis Ltd Spatial Analysis with R
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£130.06