论文标题
加速大都市，并进行轻巧的推理汇编
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
论文作者
论文摘要
为了为大都会危机构建准确的提议者马尔可夫链蒙特卡洛，我们将概率图形模型和神经网络中的思想整合在开源框架中，我们称为轻量级推理汇编（LIC）。 LIC在开放式声明性概率编程语言（PPL）中实现摊销推断。图形神经网络用于将提案分布作为马尔可夫毯子的功能进行参数化，在“汇编”期间，将其优化以近似于单位吉布斯采样分布。与先前的推理汇编（IC）不同，LIC放弃了线性执行跟踪的重要性采样，而支持直接在贝叶斯网络上操作。通过使用声明的PPL，在推理时间查询节点的马尔可夫毛毯（可能是非静态）以产生建议者实验结果表明，LIC可以生产出较少参数的提议者，对滋扰随机变量的鲁棒性以及改善后bayesian logesistic Recessions Recessions and $ n $ - $ -SCHOLSCHOLS CHOLSERCERSERPERTION。
In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-source framework we call Lightweight Inference Compilation (LIC). LIC implements amortized inference within an open-universe declarative probabilistic programming language (PPL). Graph neural networks are used to parameterize proposal distributions as functions of Markov blankets, which during "compilation" are optimized to approximate single-site Gibbs sampling distributions. Unlike prior work in inference compilation (IC), LIC forgoes importance sampling of linear execution traces in favor of operating directly on Bayesian networks. Through using a declarative PPL, the Markov blankets of nodes (which may be non-static) are queried at inference-time to produce proposers Experimental results show LIC can produce proposers which have less parameters, greater robustness to nuisance random variables, and improved posterior sampling in a Bayesian logistic regression and $n$-schools inference application.
