Sampling Strategies for Natural Resources and the Environment (Chapman & Hall/CRC Applied Environmental Statistics)

Timothy G. Gregoire

Harry T. Valentine

Chapman and Hall/CRC

This book brings together a variety of probability applications through entertaining stories that will appeal to a broad readership. What are the best stopping rules for the dating problem? What can Bayes’ formula tell us about the chances of a Champions League draw for soccer teams being rigged? How could syndicates win millions of lottery dollars by buying a multitude of tickets at the right time? What’s the best way to manage your betting bankroll in a game in which you have an edge? How to use probability to debunk quacks and psychic mediums? How can the Monte Carlo simulation be used to solve a wide variety of probability problems? Are seven riffle shuffles of a standard deck of 52 playing cards enough for randomness? <ul>
<li>Provides seventeen engaging stories that illustrate ideas in probability.</li> <li>Written so as to be suitable for those with minimal mathematical background.</li> <li>Stories can be read independently.</li> <li>Can be used as examples and exercises for teaching introductory probability.</li>
</ul>
These questions and many more are addressed in seventeen short chapters that can be read independently. The engaging stories are instructive and demonstrate valuable probabilistic ideas. They offer students material that they most likely don’t learn in class, and offer teachers a new way of teaching their subject.

Surprises in Probability: Seventeen Short Stories

Science & Math

Tijms, Henk

Mathematics

Driven by the advancement of industrial mathematics and the need for impact case studies, Inverse Problems with Applications in Science and Engineering thoroughly examines the state-of-the-art of some representative classes of inverse and ill-posed problems for partial differential equations (PDEs). The natural practical applications of this examination arise in heat transfer, electrostatics, porous media, acoustics, fluid and solid mechanics – all of which are addressed in this text. 
Features: 
Covers all types of PDEs ― namely, elliptic (Laplace’s, Helmholtz, modified Helmholtz, biharmonic and Stokes), parabolic (heat, convection, reaction and diffusion) and hyperbolic (wave) 
Excellent reference for post-graduates and researchers in mathematics, engineering and any other scientific discipline that deals with inverse problems 
Contains both theory and numerical algorithms for solving all types of inverse and ill-posed problems

Inverse Problems with Applications in Science and Engineering

Lesnic, Daniel

An Image Processing Tour of College Mathematics (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)

"An Image Processing Tour of College Mathematics aims to provide meaningful context for reviewing key topics of college mathematics curriculum, to help students gain confidence in using concepts and techniques of applied mathematics, to increase the students' awareness of recent developments in mathematical sciences, and to help students prepare for graduate studies. The topics covered include a library of elementary functions, basic concepts of descriptive statistics, probability distributions of functions of random variables, definitions and concepts behind first- and second-order derivatives, most concepts and techniques of traditional linear algebra courses, an introduction to Fourier analysis, and a variety of discrete wavelet transforms - all of that in the context of digital image processing. Features Pre-calculus material and basic concepts of descriptive statistics are reviewed in the context of image processing in the spatial domain. Key concepts of linear algebra are reviewed both in the context of fundamental operations with digital images and in the more advanced context of discrete wavelet transforms. Some of the key concepts of probability theory are reviewed in the context of image equalization and histogram matching. The convolution operation is introduced painlessly and naturally in the context of naïve filtering for denoising and is subsequently used for edge detection and image restoration. An accessible elementary introduction to Fourier analysis is provided in the context of image restoration. Discrete wavelet transforms are introduced in the context of image compression, and the readers become more aware of some of the recent developments in applied mathematics. The text helps students of mathematics ease their way into mastering the basics of scientific computer programming"--

Computers & Technology

Galperin, Yevgeniy V.

Computer Science

Analyzing Baseball Data with R, Second Edition (Chapman & Hall/CRC The R Series)

Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. 
The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online. 
New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses. 
Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. 
Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. 
Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

Albert, Jim

Baumer, Benjamin S.

Real-World Evidence in Drug Development and Evaluation (Chapman & Hall/CRC Biostatistics Series)

Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field.\nWritten specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions. 
Features Provides the first book and a single source of information on RWE in drug development Covers a broad array of topics on outcomes- and value-based RWE assessments Demonstrates proper Bayesian application and causal inference for real-world data (RWD) Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

Health, Fitness & Dieting

Diseases & Physical Ailments

The goal of this Third Edition is the same as previous editions: to provide a good foundation - and a joyful experience! - for anyone who'd like to learn about nonlinear dynamics and chaos from an applied perspective. 
The presentation stresses analytical methods, concrete examples, and geometric intuition. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, strange attractors, and synchronization. 
The prerequisites are comfort with multivariable calculus and linear algebra, as well as a first course in physics. 
Changes to this edition include substantial exercises about conceptual models of climate change, an updated treatment of the SIR model of epidemics, and amendments (based on recent research) about the Selkov model of oscillatory glycolysis. Equations, diagrams, and explanations have been reconsidered and often revised. There are also about 50 new references, many from the recent literature. 
The most notable change is a new chapter about the Kuramoto model. 
This icon of nonlinear dynamics, introduced in 1975 by the Japanese physicist Yoshiki Kuramoto, is one of the rare examples of a high-dimensional nonlinear system that can be solved by elementary means. It provides an entrée to current research on complex systems, synchronization, and networks, yet is accessible to newcomers. 
Students and teachers have embraced the book in the past for its exceptional clarity and rich applications, and its general approach and framework continue to be sound.

Nonlinear Dynamics and Chaos

Strogatz, Steven H.

Essentials of Blockchain Technology

Blockchain technologies, as an emerging distributed architecture and computing paradigm, have accelerated the development/application of the Cloud/GPU/Edge Computing, Artificial Intelligence, cyber physical systems, social networking, crowdsourcing and crowdsensing, 5G, trust management, and finance. The popularity and rapid development of Blockchain brings many technical and regulatory challenges for research and academic communities. This book will feature contributions from experts on topics related to performance, benchmarking, durability, robustness, as well data gathering and management, algorithms, analytics techniques for transactions processing, and implementation of applications.

Directional data arise in the form of circular / semicircular / axial, symmetric / asymmetric, uni / bimodal data, in practical situations of varied fields. For the purpose of modeling such kind of data sets, the data scientists found that existing models as inadequate. As there is paucity of angular models, and to fill the gap, this book is designed at constructing new angular models with the existing techniques and to develop new tools of constructing angular models with an application to control charts in angular models. This book is planned to cover the following topics in nine chapters <ul>
<li>Wrapped, stereographic and offset circular models</li> <li>Construction of angular models using Rising Sun function, positive definite sequences, discretization and through differential approach</li> <li>Extemporaneous Semicircular / arc and asymmetric l – axial models</li> <li>Choice of angular models as an inferential aspect and construction of control charts for angular data as an application are presented.</li> </ul>
This graduate level book will be useful for data scientists, researchers and research students of Statistics and allied fields.

Angular Statistics

Rao, A V Dattatreya

GIRIJA, S V S

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: <ul> <li>Classical survival analysis techniques for estimating statistical functional and hypotheses testing</li> <li>Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc.</li> <li>Information criteria to facilitate model selection including Akaike, Bayes, and Focused</li> <li>Penalized methods</li> <li>Survival trees and ensemble techniques of bagging, boosting, and random survival forests</li> <li>A brief exposure of neural networks for survival data</li> <li>R program illustration throughout the book</li>

Survival Analysis

Tattar, Prabhanjan Narayanachar

Vaman, H J

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