Beyond Traditional Probabilistic Methods in Economics
Language: English
Published by Springer, 2018
Series: Book 313 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
£ 305.50
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Add to basketItem description from seller
Seller Inventory # 9783030041991
- Title
- Beyond Traditional Probabilistic Methods in Economics
- Author
- Nguyen Ngoc Thach
- Publisher
- Springer
- Publication year
- 2018
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3030041999
- ISBN 13
- 9783030041991
- Item weight
- 2,107 grams
- Dimensions
- 241x160x64 mm
- Series
- Book 313 of 538: Studies in Computational Intelligence
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."Synopsis" may belong to another edition of this title.
From the Back Cover
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."About the title" may belong to another edition of this title.
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