Probability and statistics

We are happy to present the first volume of the Handbook of Defeasible Reasoning and Uncertainty Management Systems.

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory.

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering.

In this landmark set of papers, experts from around the world present the latest and most promising approaches to both the theory and practice of effective environmental management.

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining.

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain.

Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century.

This engaging undergraduate textbook uses research relevant to students'' lives to teach research methodology.

This textbook offers a unique compendium of measurement procedures for experimental data acquisition.

This book provides a comprehensive guide on how geographic information systems (GIS) can be used to optimize solar energy resources.

Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century.

This textbook discusses central statistical concepts and their use in business and economics.

The book offers insight into the healthcare system by exploring emerging technologies and AI-based applications and implementation strategies.

The book is organized so as to address in separate sections first the preparatory topics of medicine (clinical and epidemiological), science in general, and statistics (mathematical); then topics of epidemiological research proper; and, finally, topics of 'meta-epidemiological' clinical research.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science.

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Over the course of his distinguished career, Robert Strichartz (1943-2021) had a substantial impact on the field of analysis with his deep, original results in classical harmonic, functional, and spectral analysis, and in the newly developed analysis on fractals.

Pattern Theory: From Representation to Inference provides a comprehensive and accessible overview of the modern challenges in signal, data and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision.

Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al.

Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people's preferences based on decision making in hypothetical choice situations.

Dieses Lehrbuch umfaßt einen Kanon von Themen, der an vielen Universitäten unter dem Titel "Diskrete Strukturen" fester Bestandteil des Informatik-Grundstudiums geworden ist.

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series.

Versatile for Several Interrelated Courses at the Undergraduate and Graduate LevelsFinancial Mathematics: A Comprehensive Treatment provides a unified, self-contained account of the main theory and application of methods behind modern-day financial mathematics.

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This wide-ranging, jargon-free dictionary contains over 2,300 entries on all aspects of statistics, including terms used in computing, mathematics, and probability.

Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day?

In real-life scenarios, service management involves complex decision-making processes usually affected by random or stochastic variables.

Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines.

VI ten Beispiele und Aufgaben überprüft.

A First Course in Ergodic Theory provides readers with an introductory course in Ergodic Theory.

The book has been tested and refined through years of classroom teaching experience.

This edition is a reprint of the second edition published in 2000 by Brooks/Cole and then Cengage Learning.

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis.

Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model.

This book takes the standard methods as the starting point, and then describes a wide range of relatively new approaches and procedures designed to deal with more complicated data and experiments - including much recent research in the area.

Originally published in 1987.

This book contains the proceedings of the conference ANNIMAB-l, held 13-16 May 2000 in Goteborg, Sweden.

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Managers need access to some statistical advice from time to time to help in solving business problems.

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems.

Acceptance Sampling in Quality Control, Third Edition presents the state of the art in the methodology of sampling while integrating both theory and best practices.

This book takes the reader through the process of learning and creating data visualisation, following a unique journey with questions every step of the way, ultimately discussing how and when to bend and break the "e;rules"e; to come up with creative, unique, and sometimes unconventional ideas.

The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, mathematics, business, engineering, and the natural and social sciences.