Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering
Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering
复制标题
贝叶斯非参数聚类的共识变分和蒙特卡罗算法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Zeya Wang
中科院分区：
文献类型：
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作者：
Yang Ni;D. Jones;Zeya Wang
为了有效地实现贝叶斯非参数混合模型，我们提出了第一种令人尴尬的并行共识变分推理算法和一种新的共识蒙特卡罗算法。所提出的算法基于群聚类方法，与标准马尔可夫链蒙特卡罗和变分推理聚类算法相比，它们大大加快了推理速度，降低了内存成本。我们证明了我们提出的算法在保持相同聚类精度的情况下比竞争方法要快得多。由于其简单性和令人尴尬的并行性，我们提出的算法易于实现，并且广泛适用于本文所考虑的模型和应用。
We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.