Probability & Statistics

Statistics is one of the most practical and essential courses that you will take, and a primary goal of this popular text is to make the task of learning statistics as simple as possible. Straightforward instruction, built-in learning aids, and...

Meta-analysis allows researchers to combine the results of several studies into a unified analysis that provides an overall estimate of the effect of interest. This collection of articles from the Stata Journal and Stata Technical Bulletin will be...

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual...

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780990637202Author Hossein Pishro-NikFormat PaperbackPage Count 748Imprint Kappa Research, LLCPublisher Kappa Research, LLC

Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less...

Financial markets are a fascinating example of 'complexity in action': a real-world complex system whose evolution is dictated by the decisions of crowds of traders who are continually trying to win in a vast global 'game'. This book draws on recent...

This book presents the fundamentals of stochastic thermodynamics, one of the most central subjects in non-equilibrium statistical mechanics. It also explores many recent advances, e.g., in information thermodynamics, the thermodynamic uncertainty...

Statistical analysis allows us to attach meaning to data that we have collected; it helps us to understand what experimental results really mean, and to assess whether we can trust what experiments seem to be telling us. Yet, despite being a collection...

In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health...

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries Key Features Conduct Bayesian data analysis with...

This textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262048019Author Marc G. BellemareFormat HardbackPage Count 400Imprint MIT PressPublisher MIT Press Ltd

This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this...

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory...

Edexcel and A Level Modular Mathematics S2 features: Student-friendly worked examples and solutions, leading up to a wealth of practice questions. Sample exam papers for thorough exam preparation. Regular review sections consolidate learning...

Provides a comprehensive coverage of both the deterministic and stochastic models of life contingencies, risk theory, credibility theory, multi-state models, and an introduction to modern mathematical finance. New edition restructures the material to fit...

Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is...

An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing...

Wouldn't it be great if there were a statistics book that made histograms, probability distributions, and chi square analysis more enjoyable than going to the dentist? "Head First Statistics" brings this typically dry subject to life, teaching you...

We may learn from our mistakes, but this work argues that, where experimental knowledge is concerned, we haven't begun to learn enough. It provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own...

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and...

This book contains the proceedings of a workshop, 'Statistical Methods for the Assess ment of Point Source Pollution', held September 12-14, 1988, at the Canada Centre for Inland Waters in Burlington, Ontario, Canada. The objectives of the workshop were...

This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how...

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780316440691Author William PoundstoneFormat PaperbackPage Count 320Imprint Little, Brown SparkPublisher Little, Brown SparkWeight(grams) 272gDimensions(mm)...

Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data...

An accessible primer on how to create effective graphics from dataThis book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how...

Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from...

In their bestselling title MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the...

Mathematics of Tabletop Games provides a bridge between mathematics and hobby tabletop gaming. Instead of focusing on games mathematicians play, such as nim and chomp, this book starts with the tabletop games played by avid gamers and hopes to address...

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten...

Data, data, data: It's all one ever hears about these days. Science is all about big data. Our bosses call out for analytics, whatever those might be. And everyone wants to predict what will happen next. Can we accurately predict if a company's stock...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780262545198Author Paul R. RosenbaumFormat PaperbackPage Count 232Imprint MIT PressPublisher MIT Press Ltd

Using the dichotomy of structure and pseudorandomness as a central theme, this accessible text provides a modern introduction to extremal graph theory and additive combinatorics. Readers will explore central results in additive combinatorics-notably the...

Statistics in Geography has established itself as the best introductory textbook on the subject: the author makes statistical concepts and techniques intellible and their applications in a wide variety of problems comprehensible, even exciting. The main...

Become more likely to succeed-gain stats mastery with Dummies Statistics: 1001 Practice Problems For Dummies gives you 1,001 opportunities to practice solving problems from all the major topics covered in Statistics classes-in the book and online! Get...

R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis...

This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for...

Many problems in biology require an understanding of the relationships among variables in a multivariate causal context. Exploring such cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses...