论文标题
与深豪斯流程的无似然推理
Likelihood-Free Inference with Deep Gaussian Processes
论文作者
论文摘要
近年来，替代模型已成功用于无可能推断，以减少模拟器评估的数量。通过高斯流程（GPS），通过贝叶斯优化实现了此任务的当前最新性能。尽管这种组合对于单峰目标分布效果很好，但它限制了贝叶斯优化的灵活性和适用性，以更加一般地加速无可能的推理。我们通过提出一个深层高斯过程（DGP）替代模型来解决这个问题，该模型可以处理更不规则的目标分布。我们的实验表明，DGP如何在具有多模式分布的目标函数上胜过GP，并在单峰情况下保持可比的性能。这证实DGP作为替代模型可以扩展贝叶斯优化对无可能推理的适用性（BOLFI），同时添加计算开销，对于计算密集型模拟器而言仍然可以忽略不计。
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations. The current state-of-the-art performance for this task has been achieved by Bayesian Optimization with Gaussian Processes (GPs). While this combination works well for unimodal target distributions, it is restricting the flexibility and applicability of Bayesian Optimization for accelerating likelihood-free inference more generally. We address this problem by proposing a Deep Gaussian Process (DGP) surrogate model that can handle more irregularly behaved target distributions. Our experiments show how DGPs can outperform GPs on objective functions with multimodal distributions and maintain a comparable performance in unimodal cases. This confirms that DGPs as surrogate models can extend the applicability of Bayesian Optimization for likelihood-free inference (BOLFI), while adding computational overhead that remains negligible for computationally intensive simulators.
