哈密尔顿算子ABC
Hamiltonian ABC
摘要：
近似贝叶斯计算（ABC）是一个强大而优雅的框架，用于在基于模拟的模型中执行推理。然而，由于在缩放似然估计的困难，ABC仍然适用于相对低维的问题。我们介绍了汉密尔顿ABC（HABC），一组无似然算法，适用于最新进展的缩放贝叶斯学习使用哈密顿蒙特卡罗（HMC）和随机梯度。我们发现，少量的前向模拟可以有效地近似ABC梯度，允许哈密顿动力学有效地遍历参数空间。我们还描述了一个新的简单而通用的方法，将随机种子的状态的马尔可夫链，进一步减少了随机游动行为的HABC。我们在几个典型的ABC问题上演示了HABC，并在一个来自机器学习的高维问题上使用真梯度证明了HABC样本对常规贝叶斯推断的支持。
英文摘要：
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood estimates, ABC remains useful for relatively low-dimensional problems. We introduce Hamiltonian ABC (HABC), a set of likelihood-free algorithms that apply recent advances in scaling Bayesian learning using Hamiltonian Monte Carlo (HMC) and stochastic gradients. We find that a small number forward simulations can effectively approximate the ABC gradient, allowing Hamiltonian dynamics to efficiently traverse parameter spaces. We also describe a new simple yet general approach of incorporating random seeds into the state of the Markov chain, further reducing the random walk behavior of HABC. We demonstrate HABC on several typical ABC problems, and show that HABC samples comparably to regular Bayesian inference using true gradients on a high-dimensional problem from machine learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
