Exploring the Accuracy of Joint-Distribution Approximations Given Partial Information

IF 1 4区经济学 Q4 BUSINESS

Engineering Economist Pub Date : 2019-02-17 DOI:10.1080/0013791X.2018.1512692

Luis V. Montiel, J. Bickel

引用次数: 2

Abstract

Abstract We test the accuracy of various methods for approximating underspecified joint probability distributions. In particular, we examine the maximum entropy and the analytic center approximations, and we introduce three methods for approximating a discrete joint probability distribution given partial probabilistic information. Our results suggest that recently proposed approximations and our new approximations more accurately represent the possible uncertainty models than do previous models such as maximum entropy.

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探讨部分信息下联合分布近似的准确性

摘要:我们测试了各种近似欠指定联合概率分布的方法的准确性。特别地，我们研究了最大熵和解析中心近似，并介绍了在给定部分概率信息的情况下近似离散联合概率分布的三种方法。我们的结果表明，最近提出的近似和我们的新近似比以前的模型(如最大熵)更准确地代表了可能的不确定性模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Economist ENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Engineering Economist is a refereed journal published jointly by the Engineering Economy Division of the American Society of Engineering Education (ASEE) and the Institute of Industrial and Systems Engineers (IISE). The journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment. The journal seeks submissions in a number of areas, including, but not limited to: capital investment analysis, financial risk management, cost estimation and accounting, cost of capital, design economics, economic decision analysis, engineering economy education, research and development, and the analysis of public policy when it is relevant to the economic investment decisions made by engineers and technology managers.