论文标题
学习随机过滤
Learning stochastic filtering
论文作者
论文摘要
我们通过Kullback-Leibler发散与最佳贝叶斯滤波器的差异量化近似值对随机过滤的性能。使用将布朗测量过程驱动为原型测试用例的两国马尔可夫工艺，我们比较了两个随机过滤近似值：静态低通滤波器作为基线，以及使用非线性矢量自动回归（NVAR）对Voltera扩展的机器学习。我们强调了所选的性能指标的关键作用，并提出了两种解决方案，以预测可能性之间的可能性在$ 0 $ 0 $至$ 1 $之间。
We quantify the performance of approximations to stochastic filtering by the Kullback-Leibler divergence to the optimal Bayesian filter. Using a two-state Markov process that drives a Brownian measurement process as prototypical test case, we compare two stochastic filtering approximations: a static low-pass filter as baseline, and machine learning of Voltera expansions using nonlinear Vector Auto Regression (nVAR). We highlight the crucial role of the chosen performance metric, and present two solutions to the specific challenge of predicting a likelihood bounded between $0$ and $1$.
