Operations Research: A Practical Introduction is just that: a hands-on approach to the field of operations research (OR) and a useful guide for using OR techniques in scientific decision making, design, analysis and management.
This practical text is an essential source of information for those wanting to know how to deal with the variability that exists in every engineering situation.
Winner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data.
These Lecture Notes provide an introduction to the study of those discrete surfaces which are obtained by randomly gluing polygons along their sides in a plane.
The reproducing kernel Hilbert space construction is a bijection or transform theory which associates a positive definite kernel (gaussian processes) with a Hilbert space offunctions.
The first edition of Theory of Rank Tests (1967) has been the precursor to a unified and theoretically motivated treatise of the basic theory of tests based on ranks of the sample observations.
Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data.
Practical Business Statistics, Sixth Edition, is a conceptual , realistic, and matter-of-fact approach to managerial statistics that carefully maintains, but does not overemphasize, mathematical correctness.
In 1991, a subcommittee of the Federal Committee on Statistical Methodology met to document the use of indirect estimators - that is, estimators which use data drawn from a domain or time different from the domain or time for which an estimate is required.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
This book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022.
The following topical subjects were discussed: quantum stochastic calculus; unbounded quantum dynamical system; the principles of nonstandard analysis that are fundamental to an understanding of a modern approach to stochastic analysis on fractals; Brownian motion on nested fractals; stable processes; stochastic modelling of sexually transmitted diseases.
Author's Note: The material of this book has been reworked and expanded with a lot more detail and published in the author's 2014 book "e;Upper and Lower Bounds for Stochastic Processes"e; (Ergebnisse Vol.
David Papineau presents a controversial view of human reason, portraying it as a normal part of the natural world, and drawing on the empirical sciences to illuminate its workings.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels.
Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time.
How to make simple sense of complex statistics--from the author of Numbers Rule Your WorldWe live in a world of Big Data--and it's getting bigger every day.
Interpreting Statistics for Beginners teaches readers to correctly read and interpret results of basic statistical procedures as they are presented in scientific literature, and to understand what they can and cannot infer from such results.
Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD).
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective.