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 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.
翻译:我们解释如何利用当代贝叶斯推断引擎,例如基于无倾斜抽样和预期传播的引擎,来构建有效的自动概率密度函数估计数。广泛的模拟研究表明,拟议的密度估计数具有极好的比较性能,而且由于采用双进制战略,其规模非常大。此外,这种方法完全是巴伊斯式的,所有估计数都附有点性可靠的间隔。用R语制作的配套软件便于使用新的密度估计数。