论文标题
与马尔可夫链蒙特卡洛模拟的稳健坐标上升变异推理
Robust Coordinate Ascent Variational Inference with Markov chain Monte Carlo simulations
论文作者
论文摘要
变异推理（VI）是一种使用更好的分布家族近似难以强调的后密度的方法。 VI是近似密度的已经良好研究的马尔可夫链蒙特卡洛（MCMC）方法的替代方法。使用每种算法，当然都有好处和缺点。是否存在减轻两者缺陷的两个组合？我们提出了一种将坐标上升变异推理（CAVI）与MCMC相结合的方法。这种称为混合Cavi的新方法试图通过使用从短MCMC燃烧时期获得的矩估计方法提出初始化，以提高CAVI初始化和收敛性问题的敏感性。与Cavi不同，当后部不来自有条件的共轭指数家族时，杂种Cavi也是有效的。
Variational Inference (VI) is a method that approximates a difficult-to-compute posterior density using better behaved distributional families. VI is an alternative to the already well-studied Markov chain Monte Carlo (MCMC) method of approximating densities. With each algorithm, there are of course benefits and drawbacks; does there exist a combination of the two that mitigates the flaws of both? We propose a method to combine Coordinate Ascent Variational Inference (CAVI) with MCMC. This new methodology, termed Hybrid CAVI, seeks to improve the sensitivity to initialization and convergence problems of CAVI by proposing an initialization using method of moments estimates obtained from a short MCMC burn-in period. Unlike CAVI, Hybrid CAVI proves to also be effective when the posterior is not from a conditionally conjugate exponential family.
