Beyond traditional probabilistic methods in economics [electronic resource] / edited by Vladik Kreinovich ... [et al.].
- 其他作者：
- 其他題名：
- Studies in computational intelligence ;
- 出版： Cham : Springer International Publishing :Imprint: Springer 2019.
- 叢書名： Studies in computational intelligence,v.809
- 主題： Economics, Mathematical. , Computational Intelligence. , Artificial Intelligence. , Economic Theory/Quantitative Economics/Mathematical Methods.
- ISBN： 9783030042004 (electronic bk.) 、 9783030041991 (paper)
- URL：
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- 系統號： 000274284 | 機讀編目格式
館藏資訊
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.
摘要註
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.