Decision Systems and Nonstochastic Randomness
Author:   V. I. Ivanenko
Publisher:   Springer-Verlag New York Inc.
Edition:   2010 ed.
ISBN:  

9781441955470


Pages:   272
Publication Date:   01 April 2010
Format:   Hardback
Availability:   In Print   Availability explained
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Decision Systems and Nonstochastic Randomness


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Overview

""Decision Systems and Non-stochastic Randomness"" is the first systematic presentation and mathematical formalization (including existence theorems) of the statistical regularities of non-stochastic randomness. The results presented in this book extend the capabilities of probability theory by providing mathematical techniques that allow for the description of uncertain events that do not fit standard stochastic models. The book demonstrates how non-stochastic regularities can be incorporated into decision theory and information theory, offering an alternative to the subjective probability approach to uncertainty and the unified approach to the measurement of information. This book is intended for statisticians, mathematicians, engineers, economists or other researchers interested in non-stochastic modeling and decision theory.

Full Product Details

Author:   V. I. Ivanenko
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2010 ed.
Dimensions:   Width: 16.80cm , Height: 2.00cm , Length: 24.00cm
Weight:   0.715kg
ISBN:  

9781441955470


ISBN 10:   144195547
Pages:   272
Publication Date:   01 April 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Decision Systems.- Indifferent Uncertainty.- Nonstochastic Randomness.- General Decision Problems.- Experiment in Decision Problems.- Informativity of Experiment in Bayesian Decision Problems.- Reducibility of Experiments in Multistep Decision Problems.- Concluding Remarks.

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