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The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

NOW A MAJOR NETFLIX FILM STARRING HALEY LU RICHARDSONWho would have guessed that four minutes could change everything?Today should be one of the worst days of Hadley's life. Her father is getting married in London to a woman she's never even met, and she's just missed her flight.Hadley has never believed in destiny or fate before. But, stuck at the airport in New York, today is also the day she meets Oliver. He's British. He's cute. And he's on her new flight.Set over twenty-four hours, Hadley and Oliver's story will make you believe that true love finds you when you're least expecting it.Readers love The Statistical Probability of Love at First Sight'A warm and witty book about destiny, first loves, soul mates and perfect timing' 5* reader review'One of those books that you just want to keep reading and reading' 5* reader review'So amazing! A beautiful love story' 5* reader review

STATISTICAL METHODS FOR PSYCHOLOGY, 8E, International Edition surveys the statistical techniques commonly used in the behavioral and social sciences, especially psychology and education. To help students gain a better understanding of the specific statistical hypothesis tests that are covered throughout the text, author David Howell emphasize conceptual understanding. Along with significantly updated discussions of effect size and meta-analysis, this Eighth Edition continues to focus on two key themes that are the cornerstones of this book's success: the importance of looking at the data before beginning a hypothesis test, and the importance of knowing the relationship between the statistical test in use and the theoretical questions being asked by the experiment.

As a graduate student working in quantum optics I encountered the question that might be taken as the theme of this book. The question definitely arose at that time though it was not yet very clearly defined; there was simply some deep irritation caused by the work I was doing, something quite fundamental I did not understand. Of course, so many things are not understood when one is a graduate student. However, my nagging question was not a technical issue, not merely a mathematical concept that was difficult to grasp. It was a sense that certain elementary notions that are accepted as starting points for work in quantum optics somehow had no fundamental foundation, no identifiable root. My inclination was to mine physics vertically, and here was a subject whose tunnels were dug horizontally. There were branches, certainly, going up and going down. Nonetheless, something major in the downwards direction was missing-at least in my understanding; no doubt others understood the connections downwards very well. In retrospect I can identify the irritation. Quantum optics deals primarily with dynamics, quantum dynamics, and in doing so makes extensive use of words like "quantum fluctuations" and "quantum noise. " The words seem harmless enough. Surely the ideas behind them are quite clear; after all, quantum mechanics is a statistical theory, and in its dynamical aspects it is therefore a theory of fluctuations. But there was my problem. Nothing in Schrodinger's equation fluctuates.

From the hydrophobic effect to protein-ligand binding, statistical physics is relevant in almost all areas of molecular biophysics and biochemistry, making it essential for modern students of molecular behavior. But traditional presentations of this material are often difficult to penetrate. Statistical Physics of Biomolecules: An Introduction brings "down to earth" some of the most intimidating but important theories of molecular biophysics. With an accessible writing style, the book unifies statistical, dynamic, and thermodynamic descriptions of molecular behavior using probability ideas as a common basis. Numerous examples illustrate how the twin perspectives of dynamics and equilibrium deepen our understanding of essential ideas such as entropy, free energy, and the meaning of rate constants. The author builds on the general principles with specific discussions of water, binding phenomena, and protein conformational changes/folding. The same probabilistic framework used in the introductory chapters is also applied to non-equilibrium phenomena and to computations in later chapters. The book emphasizes basic concepts rather than cataloguing a broad range of phenomena. Focuses on what students need to know now Students build a foundational understanding by initially focusing on probability theory, low-dimensional models, and the simplest molecular systems. The basics are then directly developed for biophysical phenomena, such as water behavior, protein binding, and conformational changes. The book’s accessible development of equilibrium and dynamical statistical physics makes this a valuable text for students with limited physics and chemistry backgrounds.

The second edition of this standard text guides biomedical researchers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is again used to perform the analyses, this time employing the much improved version 10 with its intuitive point and click as well as character-based commands. Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available at http://biostat.mc.vanderbilt.edu/dupontwd/wddtext/.

Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies , you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way!

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited tosecond or advanced courses in statistical methods across the social and behavioural sciences.

This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).

The Manchester Physics Serie s General Editors: D. J. Sandiford; F. Mandl; A. C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B. H. Flowers and E. Mendoza Optics Second Edition F. G. Smith and J. H. Thomson Statistical Physics Second Edition E. Mandl Electromagnetism Second Edition I. S. Grant and W. R. Phillips Statistics R. J. Barlow Solid State Physics Second Edition J. R. Hook and H. E. Hall Quantum Mechanics F. Mandl Particle Physics Second Edition B. R. Martin and G. Shaw The Physics of Stars Second Edition A. C. Phillips Computing for Scientists R. J. Barlow and A. R. Barnett Statistical Physics , Second Edition develops a unified treatment of statistical mechanics and thermodynamics, which emphasises the statistical nature of the laws of thermodynamics and the atomic nature of matter. Prominence is given to the Gibbs distribution, leading to a simple treatment of quantum statistics and of chemical reactions. Undergraduate students of physics and related sciences will find this a stimulating account of the basic physics and its applications. Only an elementary knowledge of kinetic theory and atomic physics, as well as the rudiments of quantum theory, are presupposed for an understanding of this book. Statistical Physics , Second Edition features: A fully integrated treatment of thermodynamics and statistical mechanics. A flow diagram allowing topics to be studied in different orders or omitted altogether. Optional "starred" and highlighted sections containing more advanced and specialised material for the more ambitious reader. Sets of problems at the end of each chapter to help student understanding. Hints for solving the problems are given in an Appendix.

A lucid presentation of statistical physics and thermodynamics which develops from the general principles to give a large number of applications of the theory.

Gain the statistics skills you need for the social sciences with this accessible introductory guide Statistical Methods for the Social Sciences, 5th Edition, Global Edition, by Alan Agresti, introduces you to statistical methods used in social science disciplines with no previous knowledge of statistics necessary. With an emphasis on concepts and applications, the book requires only a minimal mathematical background, maintaining a low technical level throughout to make it accessible to beginners. The 5th edition has a strong focus on real examples to help you learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. This approach also helps you understand how to apply your learning to the real world. This edition also emphasises the interpretation of software output rather than the formulas for performing analysis, reflecting advances in statistical software - which are more frequently used by social scientists to analyse data today. Other updates include: Numerous homework exercises included in each chapter. Updated data in most exercises. New sections, such as that on maximum likelihood estimation in chapter 5 New examples ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. The text also relies more on applets for finding tail probabilities from distributions such as the Normal, t, and chi-squared. With a wide array of learning features and the latest available information, this text will equip you with the knowledge you need to succeed in your course - an ideal companion for students majoring in social science disciplines.

Help your future genius become the smartest baby in the room! Written by an expert, Statistical Physics for Babies is a colorfully simple introduction to the second law of thermodynamics. Babies (and grownups!) will learn all about entropy, probability, and more. With a tongue-in-cheek approach that adults will love, this installment of the Baby University board book series is the perfect way to introduce basic concepts to even the youngest scientists. After all, it's never too early to become a scientist! Baby University: It only takes a small spark to ignite a child's mind.

The book presents an introduction to statistical methods for students majoring in social science disciplines. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). The book contains sufficient material for a two-semester sequence of courses. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course. Datasets and other resources (where applicable) for this book are available here.

This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.

STATISTICAL METHODS FOR PSYCHOLOGY surveys the statistical techniques commonly used in the behavioral and social sciences, especially psychology and education. To help students gain a better understanding of the specific statistical hypothesis tests that are covered throughout the text, author David Howell emphasize conceptual understanding. Along with significantly updated discussions of effect size and meta-analysis, this Eighth Edition continues to focus on two key themes that are the cornerstones of this book's success: the importance of looking at the data before beginning a hypothesis test, and the importance of knowing the relationship between the statistical test in use and the theoretical questions being asked by the experiment.

STATISTICAL METHODS FOR PSYCHOLOGY, 8E, International Edition surveys the statistical techniques commonly used in the behavioral and social sciences, especially psychology and education. To help students gain a better understanding of the specific statistical hypothesis tests that are covered throughout the text, author David Howell emphasize conceptual understanding. Along with significantly updated discussions of effect size and meta-analysis, this Eighth Edition continues to focus on two key themes that are the cornerstones of this book's success: the importance of looking at the data before beginning a hypothesis test, and the importance of knowing the relationship between the statistical test in use and the theoretical questions being asked by the experiment.

For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 6th Edition gives you examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.

Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data the fast and easy way!