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Title Beyond traditional probabilistic methods in economics / edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, Dang Van Thanh
Imprint Cham : Springer International Publishing : Imprint: Springer, 2019
book jacket
Descript 1 online resource (xiv, 1157 pages) : illustrations, digital ; 24 cm
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Series Studies in computational intelligence, 1860-949X ; volume 809
Studies in computational intelligence ; volume 809
Note This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important - and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications
Host Item Springer eBooks
Subject Economics, Mathematical
Computational Intelligence
Artificial Intelligence
Economic Theory/Quantitative Economics/Mathematical Methods
Alt Author Kreinovich, Vladik, editor
Thach, Nguyen Ngoc, editor
Trung, Nguyen Duc, editor
Van Thanh, Dang, editor
SpringerLink (Online service)
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