论文标题
混合流：通过混合流的原则变化推断
MixFlows: principled variational inference via mixed flows
论文作者
论文摘要
这项工作提出了混合变异流（混合流），这是一个新的变分家族，由地图重复应用到初始参考分布的混合物组成。首先，我们为I.I.D.提供有效的算法。采样，密度评估和无偏的ELBO估计。然后，我们表明，当流程图是奇特的且具有衡量标准时，混合流具有类似于MCMC的收敛性，并为近似流量图的实际实现提供了误差的积累界限。最后，我们基于未经校正的离散的哈密顿动力学以及确定性动量茶点来开发混合流的实现。模拟和真实的数据实验表明，混合流可以提供比几个黑盒归一化流的可靠后近似值，以及与最先进的MCMC方法获得的质量相当的样本。
This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution. First, we provide efficient algorithms for i.i.d. sampling, density evaluation, and unbiased ELBO estimation. We then show that MixFlows have MCMC-like convergence guarantees when the flow map is ergodic and measure-preserving, and provide bounds on the accumulation of error for practical implementations where the flow map is approximated. Finally, we develop an implementation of MixFlows based on uncorrected discretized Hamiltonian dynamics combined with deterministic momentum refreshment. Simulated and real data experiments show that MixFlows can provide more reliable posterior approximations than several black-box normalizing flows, as well as samples of comparable quality to those obtained from state-of-the-art MCMC methods.
