Probability and statistics

The new edition of An Introduction to Statistical Concepts is designed to help students really understand statistical concepts, the situations in which they can be used, and how to apply them to data.

This book is written for professionals who wish to learn about methodological issues associated with health care surveys.

Approach your problems from the right end It isn't that they can't see the solution.

The present study is an extension of the topic introduced in Dr.

This book teaches foundations of epidemiological design and statistical methods, as well as including topics applicable to new areas of research.

This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.

This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.

This book provides a compact introduction to the bootstrap method.

Modern ecological and environmental sciences are dominated by observational data.

This is a handbook of statistical data covering production and distribution, foreign trade, foreign indebtedness, domestic finance, energy and standards of living in Eastern Europe (including Yugoslavia) and the Soviet Union.

This book provides an accessible overview concerning the stochastic numerical methods inheriting long-time dynamical behaviours of finite and infinite-dimensional stochastic Hamiltonian systems.

Competition for research funds in epidemiology, preventive medicine and biostatistics is highly competitive and at the same time, the grant application and review process at such agencies at the National Institutes of Health (NIH) has undergone substantial revisions.

This book provides a comprehensive introduction to the theory of stochastic calculus and some of its applications.

Model-based Geostatistics for Global Public Health: Methods and Applications provides an introductory account of model-based geostatistics, its implementation in open-source software and its application in public health research.

How can we teach machine learning to identify causal patterns in data?

This undergraduate and graduate textbook provides a practical and comprehensive overview of reliability and risk analysis techniques.

Protein informatics is a newer name for an already existing discipline.

This is the first book to show the capabilities of Microsoft Excel in teaching marketing statistics effectively.

This book presents recent developments in multivariate and robust statistical methods.

Terrorist groups throughout the world have been studied primarily through the use of social science methods.

Statistical Concepts-A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition.

Modern statistical software provides the ability to compute statistics in a timely, orderly fashion.

Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery.

This book offers comprehensive information on the theory, models and algorithms involved in state-of-the-art multivariate time series analysis and highlights several of the latest research advances in climate and environmental science.

This book discusses the psychological traits associated with drug consumption through the statistical analysis of a new database with information on 1885 respondents and use of 18 drugs.

A comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies.

This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra.

Big data is presenting challenges to cybersecurity.

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments.

This book provides a visual and intuitive coverage of the core theory of linear models.

In dem Buch werden zunächst die für eine warteschlangentheoretische Modellierung eines komplexen Inbound Callcenters relevanten Komponenten inklusive ihrer Zusammenhänge und besonderen Eigenschaften beschrieben.

With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models.

This book introduces econometric analysis of cross section, time series and panel data with the application of statistical software.

Amongmanyexcitingdevelopmentsinstatisticsoverthelasttwodecades, nonlineartimeseriesanddata-analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths.

This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories.

Originally published in 1984.

Probability and Statistics for Data Science: Math + R + Data covers "e;math stat"e;-distributions, expected value, estimation etc.

This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment.

This book begins with the fundamental large sample theory, estimating ruin probability, and ends by dealing with the latest issues of estimating the Gerber-Shiu function.

Stealth technology is a crucial pre-requisite in the combat zone, where swiftness, surprise and initiative are the decisive elements for survivability.

Stochastic Tools in Mathematics and Science is an introductory book on probability-based modeling.

Stochastic ordering is a fundamental guide for decision making under uncertainty.

This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource.

This practical guidebook describes the basic concepts, the mathematical developments, and the engineering methodologies for exploiting possibility theory for the computer-based design of an information fusion system where the goal is decision support for industries in smart ICT (information and communications technologies).

Digital signal processing has become an integral part of observational seismology.