Probability Theory and Statistical Applications

Peter Zörnig

ISBN 10: 3110363194 / ISBN 13: 9783110363197

Edité par De Gruyter Jul 2016, 2016

A propos de cet article

Neuware -This accessible and easy-to-read book provides many examples to illustrate diverse topics in probability and statistics, from initial concepts up to advanced calculations. Special attention is devoted e.g. to independency of events, inequalities in probability and functions of random variables. The book is directed to students of mathematics, statistics, engineering, and other quantitative sciences, in particular to readers who need or want to learn by self-study. The author is convinced that sophisticated examples are more useful for the student than a lengthy formalism treating the greatest possible generality. Contents:Mathematics revisionIntroduction to probabilityFinite sample spacesConditional probability and independenceOne-dimensional random variablesFunctions of random variablesBi-dimensional random variablesCharacteristics of random variablesDiscrete probability modelsContinuous probability modelsGenerating functions in probabilitySums of many random variablesSamples and sampling distributionsEstimation of parametersHypothesis tests 294 pp. Englisch.

N° de réf. du vendeur 9783110363197

Détails bibliographiques

• Titre: Probability Theory and Statistical Applications
• Éditeur: De Gruyter Jul 2016
• Date de parution: 2016
• Langue: anglais
• ISBN à 10 chiffres: 3110363194
• ISBN à 13 chiffres: 9783110363197
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

This accessible and easy-to-read book provides many examples to illustrate diverse topics in probability and statistics, from initial concepts up to advanced calculations. Special attention is devoted e.g. to independency of events, inequalities in probability and functions of random variables. The book is directed to students of mathematics, statistics, engineering, and other quantitative sciences, in particular to readers who need or want to learn by self-study. The author is convinced that sophisticated examples are more useful for the student than a lengthy formalism treating the greatest possible generality.

Contents:
Mathematics revision
Introduction to probability
Finite sample spaces
Conditional probability and independence
One-dimensional random variables
Functions of random variables
Bi-dimensional random variables
Characteristics of random variables
Discrete probability models
Continuous probability models
Generating functions in probability
Sums of many random variables
Samples and sampling distributions
Estimation of parameters
Hypothesis tests

À propos de l?auteur

Peter Zörnig, University of Brasilia, Asa Norte, Brazil.