Bayes’ Rule Using Imprecise Probabilities [Lecture Notes]

IF 9.4 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Branko Ristic;Alessio Benavoli;Sanjeev Arulampalam
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引用次数: 0

Abstract

Bayes’ rule, as one of the fundamental concepts of statistical signal processing, provides a way to update our belief about an event based on the arrival of new pieces of evidence. Uncertainty is traditionally modeled by a probability distribution. Prior belief is thus expressed by a prior probability distribution, while the update involves the likelihood function, a probabilistic expression of how likely it is to observe the evidence. It has been argued by many statisticians, however, that a broadening of probability theory is required because one may not always be able to provide a probability for every event, due to the scarcity of training data.
使用不精确概率的贝叶斯法则 [讲义]
贝叶斯法则是统计信号处理的基本概念之一,它提供了一种根据新证据更新我们对某一事件的信念的方法。不确定性传统上以概率分布为模型。因此,先验概念用先验概率分布来表示,而更新则涉及似然函数,即观察到证据的可能性有多大的概率表达式。然而,许多统计学家认为,需要拓宽概率理论,因为由于训练数据的稀缺性,我们可能无法总是为每个事件提供概率。
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
IEEE Signal Processing Magazine
IEEE Signal Processing Magazine 工程技术-工程:电子与电气
CiteScore
27.20
自引率
0.70%
发文量
123
审稿时长
6-12 weeks
期刊介绍: EEE Signal Processing Magazine is a publication that focuses on signal processing research and applications. It publishes tutorial-style articles, columns, and forums that cover a wide range of topics related to signal processing. The magazine aims to provide the research, educational, and professional communities with the latest technical developments, issues, and events in the field. It serves as the main communication platform for the society, addressing important matters that concern all members.
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