Probability Theory and Statistical Applications

Peter Zörnig

ISBN 10: 3110363194 / ISBN 13: 9783110363197

Verlag: De Gruyter Jul 2016, 2016

Beschreibung

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. Bestandsnummer des Verkäufers 9783110363197

Bibliografische Details

Titel: Probability Theory and Statistical ...

Verlag: De Gruyter Jul 2016

Erscheinungsdatum: 2016

Einband: Taschenbuch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

Probability Theory And Statistical Applications : A Profound Treatise For Self-Study by Zörnig, Peter, 9783110363197, De Gruyter, 2016, Paperback

Reseña del editor:

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