Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference
Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference
复制标题
Bean Machine：一种用于高效可编程推理的声明式概率编程语言
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Eric Lippert
中科院分区：
文献类型：
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作者：
N. Tehrani;Nimar S. Arora;David Noursi;Michael Tingley;Narjes Torabi;Eric Lippert
最近提出了一些命令式概率编程语言，但命令式风格的选择使得推导潜在变量之间的依赖结构变得非常困难，这种依赖结构也可以随迭代而改变。我们提出了一种新的声明式PPL，Bean Machine，并证明在这种新的语言中，动态依赖结构是现成的。尽管我们不是第一个提出声明性PPL或观察到了解依赖结构的优势的fi，但我们通过展示在这种风格中变得可行或更容易的其他推理技术来进一步说明这一想法。我们表明，对于用户来说，在声明性语言中通过组合(针对模型的不同部分结合不同的推理技术)、定制(为特定的fic变量提供定制的手写推理方法)和分块(指定应该一起采样的随机变量块)来编写推理是非常容易的。我们提供了大量的实验结果，其中我们支持这些声明模非矢量化的fi的运行时inef依赖关系。作为一个附带的好处，我们注意到，将以数学符号编写的统计模型翻译成我们的语言是非常容易的。
A number of imperative Probabilistic Programming Languages (PPLs) have been recently proposed, but the imperative style choice makes it very hard to deduce the dependence structure be-tween the latent variables, which can also change from iteration to iteration. We propose a new declarative style PPL, Bean Machine, and demonstrate that in this new language, the dynamic dependence structure is readily available. Although we are not the first to propose a declarative PPL or to observe the advantages of knowing the dependence structure, we take the idea further by showing other inference techniques that become feasible or easier in this style. We show that it is very easy for users to program inference by composition (combining different inference techniques for different parts of the model), customization (providing a custom hand-written inference method for specific variables), and blocking (specifying blocks of random variables that should be sampled together) in a declarative language. A number of empirical results are provided where we backup these claims modulo the runtime inefficiencies of unvectorized Python. As a fringe benefit, we note that it is very easy to translate statistical models written in mathematical notation into our language.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen