Learn the basics of white noise theory with White Noise Distribution Theory. This book covers the mathematical foundation and key applications of white noise theory without requiring advanced knowledge in this area. This instructive text specifically focuses on relevant application topics such as integral kernel operators, Fourier transforms, Laplacian operators, white noise integration, Feynman integrals, and positive generalized functions. Extremely well-written by one of the field's leading researchers, White Noise Distribution Theory is destined to become the definitive introductory resource on this challenging topic.

This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences. The work considers the individual components of Bayesian analysis.;College or university bookstores may order five or more copies at a special student price, available on request from Marcel Dekker, Inc.

This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.

Multiple Comparisons covers all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. Confidence intervals methods and stepwise methods are described. Abuses and misconceptions are exposed, and the reader is guided to the correct method for each problem. Connections with bioequivalence, drug stability, and toxicity studies are discussed. Applications are illustrated with real data, analyzed by computer packages. Extension to the General Linear Model is provided.

The authoritative contributions gathered in this volume reflect the state of the art in compositional data analysis (CoDa). The respective chapters cover all aspects of CoDa, ranging from mathematical theory, statistical methods and techniques to its broad range of applications in geochemistry, the life sciences and other disciplines. The selected and peer-reviewed papers were originally presented at the 6th International Workshop on Compositional Data Analysis, CoDaWork 2015, held in L'Escala (Girona), Spain. Compositional data is defined as vectors of positive components and constant sum, and, more generally, all those vectors representing parts of a whole which only carry relative information. Examples of compositional data can be found in many different fields such as geology, chemistry, economics, medicine, ecology and sociology. As most of the classical statistical techniques are incoherent on compositions, in the 1980s John Aitchison proposed the log-ratio approach to CoDa. This became the foundation of modern CoDa, which is now based on a specific geometric structure for the simplex, an appropriate representation of the sample space of compositional data. The International Workshops on Compositional Data Analysis offer a vital discussion forum for researchers and practitioners concerned with the statistical treatment and modelling of compositional data or other constrained data sets and the interpretation of models and their applications. The goal of the workshops is to summarize and share recent developments, and to identify important lines of future research.

Counting: The Art of Enumerative Combinatorics provides an introduction to discrete mathematics that addresses questions that begin, How many ways are there to...For example, ¿How many ways are there to order a collection of 12 ice cream cones if 8 flavors are available?¿ At the end of the book the reader should be able to answer such nontrivial counting questions as, ¿How many ways are there to color the faces of a cube if ¿k¿ colors are available with each face having exactly one color?¿ or ¿How many ways are there to stack ¿n¿ poker chips, each of which can be red, white, blue, or green, such that each red chip is adjacent to at least 1 green chip?¿ Since there are no prerequisites, this book can be used for college courses in combinatorics at the sophomore level for either computer science or mathematics students. The first five chapters have served as the basis for a graduate course for in-service teachers. Chapter 8 introduces graph theory.

In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation.
Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application.
Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains.
Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.

This book is for students taking either a first-year graduate statistics course or an advanced undergraduate statistics course in Psychology. Enough introductory statistics is briefly reviewed to bring everyone up to speed. The book is highly user-friendly without sacrificing rigor, not only in anticipating students' questions, but also in paying attention to the introduction of new methods and notation. In addition, many topics given only casual or superficial treatment are elaborated here, such as: the nature of interaction and its interpretation, in terms of theory and response scale transformations; generalized forms of analysis of covariance; extensive coverage of multiple comparison methods; coverage of nonorthogonal designs; and discussion of functional measurement. The text is structured for reading in multiple passes of increasing depth; for the student who desires deeper understanding, there are optional sections; for the student who is or becomes proficient in matrix algebra, there are still deeper optional sections. The book is also equipped with an excellent set of class-tested exercises and answers.

The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.

How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive models? Build beginning-to-end workflows for predictive modeling using text as features Compare traditional machine learning methods and deep learning methods for text data

Second Edition offers a comprehensive presentation of scientific sampling principles and shows how to design a sample survey and analyze the resulting data. Demonstrates the validity of theorems and statements without resorting to detailed proofs.

'Et moi, ..., si j' avait su comment en revenir, One service mathematics has rendered the human race. It has put common sense back je n'y serais point aIle.' Jules Verne where it belongs, on the topmost shelf next to the dusty canister labelled 'discarded non- The series is divergent; therefore we may be sense'" able 10 do something with it. Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound_ Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series."

Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.

This work presents invited contributions from the second "International Conference on Mathematics and Statistics" jointly organized by the AUS (American University of Sharjah) and the AMS (American Mathematical Society). Addressing several research fields across the mathematical sciences, all of the papers were prepared by faculty members at universities in the Gulf region or prominent international researchers. The current volume is the first of its kind in the UAE and is intended to set new standards of excellence for collaboration and scholarship in the region.

This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering.

The first chapter contains a summary of basic probability theory. Chapters two to five deal with random variables and their applications. Topics covered include geometric probability, estimation of animal and plant populations, reliability theory and computer simulation. Chapter six contains a lucid account of the convergence of sequences of random variables, with emphasis on the central limit theorem and the weak law of numbers. The next four chapters introduce random processes, including random walks and Markov chains illustrated by examples in population genetics and population growth.

This edition also includes two chapters which introduce, in a manifestly readable fashion, the topic of stochastic differential equations and their applications.

This book deals with two important branches of mathematics, namely, logic and set theory. Logic and set theory are closely related and play very crucial roles in the foundation of mathematics, and together produce several results in all of mathematics. The topics of logic and set theory are required in many areas of physical sciences, engineering, and technology. The book offers solved examples and exercises, and provides reasonable details to each topic discussed, for easy understanding. The book is designed for readers from various disciplines where mathematical logic and set theory play a crucial role. The book will be of interested to students and instructors in engineering, mathematics, computer science, and technology.

Focused on practical matters: this book will not cover Shiny concepts, but practical tools and methodologies to use for production. Based on experience: this book will be a formalization of several years of experience building Shiny applications. Original content: this book will present new methodology and tooling, not just do a review of what already exists.

This work describes several statistical techniques for studying repeated measures data, presenting growth curve methods applicable to biomedical, social, animal, agricultural and business research. It details the multivariate development of growth science and repeated measures experiments, covering time-moving covariates, exchangable errors, bioassay results, missing data procedures and nonparametric and Bayesian methods.

"Offers a comprehensive, unified presentation of statistical designs and methods of analysis for all stages of pharmaceutical development--emphasizing biopharmaceutical applications and demonstrating statistical techniques with real-world examples."

Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today.
Some of these papers seek to clarify the relationships between Bayesian, causal and logical reasoning. Others consider the application of Bayesianism to artificial intelligence, decision theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. The upshot is a plethora of new problems and directions for Bayesians to pursue.

The book will be of interest to graduate students or researchers who wish to learn more about Bayesianism than can be provided by introductory textbooks to the subject. Those involved with the applications of Bayesian reasoning will find essential discussion on the validity of Bayesianism and its limits, while philosophers and others interested in pure reasoning will find new ideas on normativity and the logic of belief.

Stochastic elasticity is a fast developing field that combines nonlinear elasticity and stochastic theories in order to significantly improve model predictions by accounting for uncertainties in the mechanical responses of materials. However, in contrast to the tremendous development of computational methods for large-scale problems, which have been proposed and implemented extensively in recent years, at the fundamental level, there is very little understanding of the uncertainties in the behaviour of elastic materials under large strains. Based on the idea that every large-scale problem starts as a small-scale data problem, this book combines fundamental aspects of finite (large-strain) elasticity and probability theories, which are prerequisites for the quantification of uncertainties in the elastic responses of soft materials. The problems treated in this book are drawn from the analytical continuum mechanics literature and incorporate random variables as basic concepts along with mechanical stresses and strains. Such problems are interesting in their own right but they are also meant to inspire further thinking about how stochastic extensions can be formulated before they can be applied to more complex physical systems.

The book that follows is an experiment in the teaching of population theory and analysis. A sequence of problems where each is a self-contained puzzle, and the successful solution of each which puts the student in a position to tackle the next, is a means of securing the active participation of the learner and so the mastery of a technical subject. How far our questions are the exciting puzzles at which we aimed, and how far the sequence constitutes a rounded course in demography, must be left to the user to judge. One test of a good problem is whether a solution, that may take hours of cogitation, is immediately recognizable once it comes to mind. While algebraic manipulation is required throughout, we have tried to emphasize problems in which there is some substantive point-a conclusion regarding population that can be put into words. Our title, Demography Through Problems, reflects our intention of leading the reader who will actively commit him-or herself through a sequence that will not only teach definitions-in itself a trivial matter-but sharpen intuition on the way that populations behave.

This book describes an interactive statistical computing environment called 1 XploRe. As the name suggests, support for exploratory statistical analysis is given by a variety of computational tools. XploRe is a matrix-oriented statistical language with a comprehensive set of basic statistical operations that provides highly interactive graphics, as well as a programming environ ment for user-written macros; it offers hard-wired smoothing procedures for effective high-dimensional data analysis. Its highly dynamic graphic capa bilities make it possible to construct student-level front ends for teaching basic elements of statistics. Hot keys make it an easy-to-use computing environment for statistical analysis. The primary objective of this book is to show how the XploRe system can be used as an effective computing environment for a large number of statistical tasks. The computing tasks we consider range from basic data matrix manipulations to interactive customizing of graphs and dynamic fit ting of high-dimensional statistical models. The XploRe language is similar to other statistical languages and offers an interactive help system that can be extended to user-written algorithms. The language is intuitive and read ers with access to other systems can, without major difficulty, reproduce the examples presented here and use them as a basis for further investigation.

This book introduces readers to a new approach to identifying stock market bubbles by using the illiquidity premium, a parameter derived by employing conic finance theory. Further, it shows how to develop the closed form formulas of the bid and ask prices of European options by using Black-Scholes and Kou models. By using the derived formulas and sliding windows technique, the book explains how to numerically calculate illiquidity premiums. The methods introduced here will enable readers interested in risk management, portfolio optimization and hedging in real-time to identify when asset prices are in a bubble state and when that bubble bursts. Moreover, the techniques discussed will allow them to accurately recognize periods of exuberance and panic, and to measure how different strategies work during these phases with respect to calmer periods of market behavior. A brief history of financial bubbles and an outlook on future developments serve to round out the coverage.

This book provides a guide to such budding social researchers, who are non-native English speakers drawing examples from literature to show how to conduct a research, present research results, integrate with existing literature to draw conclusions through real-world examples. Existing English books teaching research methods and philosophy of academic research are written in 'academic English' and, it is hard for non-native English-speaking budding researchers to study and understand those books. Also, this book uses examples to show how to communicate with journal editors and peer reviewers to get published the research results as journal articles, book chapters or conference papers. This book connects different quantitative techniques, qualitative methodologies (case studies, phenomenology and ethnography and Grounded theory) as well as Mixed methods methodology through a single example. This book attempts to describe a holistic approach introducing a 10Ps model that incorporates the essential elements of the research process. The process focuses on combining philosophical framework and arguments from research results. This book focuses not only on conducting a research project, but also on the approach and procedures to be followed to achieve higher marks for course work assignments and publishing research articles in international journals. This book shows how to create many papers from one research/data set to increase number of publications and citations. This book has fewer words and more illustrations, tables, figures, pictures and YouTube tutorial links. This book outlines how to present test results in APA style for all the statistical test used in this book, using examples.