论文标题
伯恩斯坦流向各种贝叶斯的柔性后代
Bernstein Flows for Flexible Posteriors in Variational Bayes
论文作者
论文摘要
变异推理（VI）是一种难以通过优化计算后期的技术。与MCMC相反，VI量表到许多观察结果。然而，在复杂的后代，最新的VI方法通常会产生不令人满意的后近似值。本文介绍了Bernstein流量变异推理（BF-VI），这是一种健壮且易于使用的方法，足够灵活，足以近似复杂的多元后倍数。 BF-VI结合了标准化流量和伯恩斯坦多项式转换模型的想法。在基准实验中，我们将BF-VI溶液与精确的后代，MCMC溶液和最新的VI方法进行比较，包括基于流动的VI。我们显示了BF-VI准确近似真正的后部的低维模型；在高维模型中，BF-VI的表现优于其他VI方法。此外，我们使用BF-VI A开发半结构化黑素瘤挑战数据的贝叶斯模型，将图像数据的CNN模型零件与为表格数据的可解释模型组合在一起，并首次证明了如何在半结构化模型中使用VI。
Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization. In contrast to MCMC, VI scales to many observations. In the case of complex posteriors, however, state-of-the-art VI approaches often yield unsatisfactory posterior approximations. This paper presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use method, flexible enough to approximate complex multivariate posteriors. BF-VI combines ideas from normalizing flows and Bernstein polynomial-based transformation models. In benchmark experiments, we compare BF-VI solutions with exact posteriors, MCMC solutions, and state-of-the-art VI methods including normalizing flow based VI. We show for low-dimensional models that BF-VI accurately approximates the true posterior; in higher-dimensional models, BF-VI outperforms other VI methods. Further, we develop with BF-VI a Bayesian model for the semi-structured Melanoma challenge data, combining a CNN model part for image data with an interpretable model part for tabular data, and demonstrate for the first time how the use of VI in semi-structured models.
