论文标题
差异贝叶斯蒙特卡洛与嘈杂的可能性
Variational Bayesian Monte Carlo with Noisy Likelihoods
论文作者
论文摘要
变分贝叶斯蒙特卡洛（VBMC）是一个最近引入的框架，它使用高斯工艺代理来在具有黑盒，非廉价可能性的模型中执行近似贝叶斯的推断。在这项工作中，我们扩展了VBMC来处理嘈杂的对数可能评估，例如由基于模拟的模型引起的。我们介绍了新的“全局”采集功能，例如预期信息增益（EIG）和Quariational Interpretile范围（VIQR），它们对噪声非常强大，可以在VBMC设置中有效评估。在一个小说，具有挑战性的嘈杂推理基准中，包括各种模型，这些模型具有来自计算和认知神经科学的真实数据集，VBMC+VIQR在恢复地面真实的后代和模型证据方面实现了最先进的表现。特别是，我们的方法极大地优于“本地”采集功能和其他基于替代的推理方法，同时保持小算法成本。我们的基准测试证实了VBMC作为一种通用技术，用于使用嘈杂模型的样品有效的黑盒贝叶斯推断。
Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods. In this work, we extend VBMC to deal with noisy log-likelihood evaluations, such as those arising from simulation-based models. We introduce new `global' acquisition functions, such as expected information gain (EIG) and variational interquantile range (VIQR), which are robust to noise and can be efficiently evaluated within the VBMC setting. In a novel, challenging, noisy-inference benchmark comprising of a variety of models with real datasets from computational and cognitive neuroscience, VBMC+VIQR achieves state-of-the-art performance in recovering the ground-truth posteriors and model evidence. In particular, our method vastly outperforms `local' acquisition functions and other surrogate-based inference methods while keeping a small algorithmic cost. Our benchmark corroborates VBMC as a general-purpose technique for sample-efficient black-box Bayesian inference also with noisy models.
