论文标题
马尔可夫链的非参数密度估计
Nonparametric Density Estimation from Markov Chains
论文作者
论文摘要
我们引入了一个新的非参数密度估计器，灵感来自马尔可夫链，并概括了众所周知的内核密度估计器（KDE）。我们的估计器在通常的估计器上提供了一些好处，可以直接用作所有基于密度的算法的基础。我们证明了我们的估计器的一致性，并且在大型样本量和高维度的情况下，通常会优于KDE。我们还采用密度估计器来构建局部离群检测器，当应用于某些现实数据集时显示出非常有希望的结果。
We introduce a new nonparametric density estimator inspired by Markov Chains, and generalizing the well-known Kernel Density Estimator (KDE). Our estimator presents several benefits with respect to the usual ones and can be used straightforwardly as a foundation in all density-based algorithms. We prove the consistency of our estimator and we find it typically outperforms KDE in situations of large sample size and high dimensionality. We also employ our density estimator to build a local outlier detector, showing very promising results when applied to some realistic datasets.
