Self-tuning density estimation based on Bayesian averaging of adaptive kernel density estimations yields state-of-the-art performance
作者：
Highlights：
• A new method called ADEBA for multivariate adaptive density estimation is presented.
• A simulation study shows that ADEAB is competitve to currently dominating methods.
• ADEBA is simple and computes much faster than Gaussian mixture modeling.
• Further improvements can be made by incorporating application-specific prior knowledge into ADEBA.
• Implementations of ADEBA are publicly available for R.
摘要
•A new method called ADEBA for multivariate adaptive density estimation is presented.•A simulation study shows that ADEAB is competitve to currently dominating methods.•ADEBA is simple and computes much faster than Gaussian mixture modeling.•Further improvements can be made by incorporating application-specific prior knowledge into ADEBA.•Implementations of ADEBA are publicly available for R.
论文关键词：Adaptive density estimation,Variable bandwidth,Bayesian model averaging,Square root law,Multivariate,Univariate
论文评审过程：Received 1 May 2017, Revised 30 December 2017, Accepted 7 January 2018, Available online 8 January 2018, Version of Record 3 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.01.008
