In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation. We call this framework "refined variational approximation". Its strengths are its ease of implementation and the automatic tuning of sampler parameters, leading to a faster mixing time through automatic differentiation. Several strategies to approximate evidence lower bound (ELBO) computation are also introduced. Its efficient performance is showcased experimentally using state-space models for time-series data, a variational encoder for density estimation and a conditional variational autoencoder as a deep Bayes classifier.

中文翻译：

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通过细化界限进行变分推断采样


在这项工作中，通过在变分后验近似中嵌入马尔可夫链采样器，引入了一个提高概率模型中贝叶斯推理效率的框架。我们称这个框架为“精炼变分近似”。其优点是易于实施和自动调整采样器参数，通过自动微分实现更快的混合时间。还介绍了几种近似证据下界（ELBO）计算的策略。使用时间序列数据的状态空间模型、用于密度估计的变分编码器和作为深度贝叶斯分类器的条件变分自动编码器，通过实验展示了其高效性能。