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Titlebook: Beyond Traditional Probabilistic Methods in Economics; Vladik Kreinovich,Nguyen Ngoc Thach,Dang Van Thanh Conference proceedings 2019 Spri

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發(fā)表于 2025-3-21 19:27:44 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Beyond Traditional Probabilistic Methods in Economics
影響因子2023Vladik Kreinovich,Nguyen Ngoc Thach,Dang Van Thanh
視頻videohttp://file.papertrans.cn/186/185294/185294.mp4
發(fā)行地址Includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2019), held in Ho Chi Minh City, Vietnam on January 14–16, 2019.Presents recent research on probabilisti
學(xué)科分類Studies in Computational Intelligence
圖書封面Titlebook: Beyond Traditional Probabilistic Methods in Economics;  Vladik Kreinovich,Nguyen Ngoc Thach,Dang Van Thanh Conference proceedings 2019 Spri
影響因子.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 rel
Pindex Conference proceedings 2019
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Why Threshold Models: A Theoretical Explanationar (.) models, in which we have several different linear dependencies in different domains. Surprisingly, such piece-wise linear models often work better than more traditional models of non-linearity – e.g., models that take quadratic terms into account. In this paper, we provide a theoretical explanation for this empirical success.
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Beyond Traditional Probabilistic Methods in Econometricser presenting a panorama of well-known non traditional probabilistic methods, we focus on the emerging effort of taking the analogy of financial econometrics with quantum mechanics to exhibit the promising use of quantum probability for modeling human behavior, and of Bohmian mechanics for modeling economic data.
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