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>Categorical Soft Data Fusion via Variational Bayesian Importance Sampling, with Applications to Cooperative Search
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Categorical Soft Data Fusion via Variational Bayesian Importance Sampling, with Applications to Cooperative Search
This paper considers Bayesian data fusion with categorical 'soft sensor' information, such as human input in cooperative multi-agent search applications. Previous work studied variational Bayesian (VB) hybrid data fusion, which produces optimistic posterior covariance estimates and requires simple Gaussian priors with softmax likelihoods. Here, a new hybrid fusion procedure, known as variational Bayesian importance sampling (VBIS), is introduced to combine the strengths of VB and fast Monte Carlo methods to produce more reliable Gaussian posterior approximations for Gaussian priors and softmax likelihoods. VBIS is then generalized to problems involving complex Gaussian mixture priors and multimodal softmax observation models to obtain reliable Gaussian mixture posterior approximations. The utility and accuracy of the VBIS fusion method is demonstrated on a multitarget search problem for a real cooperative human-automaton team.
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机译：本文考虑了与分类的“软传感器”信息的贝叶斯数据融合，例如合作多智能经纪人搜索应用中的人类输入。以前的工作研究了变分贝叶斯（VB）混合数据融合，其产生乐观的后协方差估计，需要具有软MAX可能性的简单高斯前脚。这里，引入了一种新的混合融合程序，称为变形贝叶斯的重要性采样（VBIS），以结合VB和FAST Monte Carlo方法的强度，以产生高斯前锋和软墨型可能性的更可靠的高斯后近似。然后将VBIS推广到涉及复杂的高斯混合前的问题和多峰软墨西哥观察模型的问题，以获得可靠的高斯混合物后近似。 VBIS Fusion方法的实用性和准确性在一个真正的合作人体自动机组团队的多元搜索问题上进行了说明。
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