.png){kind=icon}
返回
Nearly-Linear uncertainty measures
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2019.08.001.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Several easy to understand and computationally tractable imprecise probability models, like the Pari-Mutuel model, are derived from a given probability measure P-0. In this paper we investigate a family of such models, called Nearly-Linear (NL). They generalise a number of well-known models, while preserving a simple mathematical structure. In fact, they are linear affine transformations of P-0 as long as the transformation returns a value in [0,1]. We study the properties of NL measures that are (at least) capacities, and show that they can be partitioned into three major subfamilies. We investigate their consistency, which ranges from 2-coherence, the minimal condition satisfied by all, to coherence, and the kind of beliefs they can represent. There is a variety of different situations that NL models can incorporate, from generalisations of the Pari-Mutuel model, the epsilon-contamination model and other models to conflicting attitudes of an agent towards low/high P-0-probability events (both prudential and imprudent at the same time), or to symmetry judgments. The consistency properties vary with the beliefs represented, but not strictly: some conflicting and partly irrational moods may be compatible with coherence. In a final part, we compare NL models with their closest, but only partly overlapping, models, neo-additive capacities and probability intervals. (C) 2019 Elsevier Inc. All rights reserved.
Keyword:
Nearly-Linear models
Pari-Mutuel model
2-coherent and coherent imprecise probabilities
Probability intervals
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
3.0K
被引数:
5.1K
机构
引用论文
.png){kind=icon}
查看更多内容