Fusion of probabilistic unreliable indirect information into estimation serving to decision making

被引:0
作者
Miroslav Kárný
František Hůla
机构
[1] Institute of Information Theory and Automation,The Czech Academy of Sciences
来源
International Journal of Machine Learning and Cybernetics | 2021年 / 12卷
关键词
Kullback–Leibler divergence; Distributed data fusion; Information fusion; Multi-agent; Parameter estimation; Decision making; Bayesian paradigm;
D O I
暂无
中图分类号
学科分类号
摘要
Bayesian decision making (DM) quantifies information by the probability density (pd) of treated variables. Gradual accumulation of information during acting increases the DM quality reachable by an agent exploiting it. The inspected accumulation way uses a parametric model forecasting observable DM outcomes and updates the posterior pd of its unknown parameter. In the thought multi-agent case, a neighbouring agent, moreover, provides a privately-designed pd forecasting the same observation. This pd may notably enrich the information of the focal agent. Bayes’ rule is a unique deductive tool for a lossless compression of the information brought by the observations. It does not suit to processing of the forecasting pd. The paper extends solutions of this case. It: ▹\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\triangleright$$\end{document} refines the Bayes’-rule-like use of the neighbour’s forecasting pd ▹\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\triangleright$$\end{document} deductively complements former solutions so that the learnable neighbour’s reliability can be taken into account ▹\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\triangleright$$\end{document} specialises the result to the exponential family, which shows the high potential of this information processing ▹\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\triangleright$$\end{document} cares about exploiting population statistics.
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页码:3367 / 3378
页数:11
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