论文标题
部分可观测时空混沌系统的无模型预测
Extremely expensive likelihoods: A variational-Bayes solution for precision cosmology
论文作者
论文摘要
我们提出了一种差异解决方案，以从极为昂贵的可能性中计算非高斯后期。当MCMC采样在数值上或概念上不可行时，我们的方法是参数推断的替代方法。例如，当无法以任意参数值评估的可能性或理论模型，而只有先前选择的值，那么传统的MCMC采样是不可能的，而我们的变异 - bayes解决方案仍然成功地估计了完整的后部。在宇宙学中，这发生了，例如当参数模型基于对先前选择的输入参数运行的昂贵模拟时。我们证明了我们的后验构建在儿童450弱镜头分析上的适用性，在该分析中，我们以其以前的数值后验评估的0.6％重建了原始的儿童MCMC后验。数值成本的降低表明，现在可以包括以前用尽数值预算的系统效应。
We present a variational-Bayes solution to compute non-Gaussian posteriors from extremely expensive likelihoods. Our approach is an alternative for parameter inference when MCMC sampling is numerically prohibitive or conceptually unfeasible. For example, when either the likelihood or the theoretical model cannot be evaluated at arbitrary parameter values, but only previously selected values, then traditional MCMC sampling is impossible, whereas our variational-Bayes solution still succeeds in estimating the full posterior. In cosmology, this occurs e.g. when the parametric model is based on costly simulations that were run for previously selected input parameters. We demonstrate the applicability of our posterior construction on the KiDS-450 weak lensing analysis, where we reconstruct the original KiDS MCMC posterior at 0.6% of its former numerical posterior evaluations. The reduction in numerical cost implies that systematic effects which formerly exhausted the numerical budget could now be included.
