论文标题
通过贝叶斯推理引擎估计密度估算
Density Estimation via Bayesian Inference Engines
论文作者
论文摘要
我们解释了如何使用当代贝叶斯推理引擎（例如基于No-U-Turn采样和期望传播）构建有效的自动概率密度函数估计。广泛的仿真研究表明，由于融合策略，提出的密度估计值具有出色的比较性能，并且可以很好地扩展到非常大的样本量。此外，该方法是完全贝叶斯的，所有估计均伴随着尖的可靠间隔。 R语言中的随附软件包有助于轻松使用新的密度估算。
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due to a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by pointwise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.
