We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation programming to address this. In expectation programming, the aim of the backend inference engine is to directly estimate expected return values of programs, as opposed to approximating their conditional distributions. This distinction, while subtle, allows us to achieve substantial performance improvements over the standard PPS computational pipeline by tailoring computation to the expectation we care about. We realize a particular instance of our expectation programming concept, Expectation Programming in Turing (EPT), by extending the PPS Turing to allow so-called target-aware inference to be run automatically. We then verify the statistical soundness of EPT theoretically, and show that it provides substantial empirical gains in practice.
翻译:我们发现,概率性方案编制系统的标准计算管道(PPS)在估计预期值方面可能效率低下,并引入了解决这一问题的预期方案编制概念。 在预测性方案编制中,后端推断引擎的目标是直接估计方案的预期回报值,而不是接近其有条件分布。 这一区别虽然微妙,但使我们能够通过根据我们所关心的预期进行计算,大大改进PPPS标准计算管道的业绩。 我们意识到我们预期的方案编制概念“图灵预期规划”的一个特例,即延长PPPS图灵,允许所谓的目标意识推断自动运行。 我们随后核查了EPT理论上的统计可靠性,并表明它在实践中提供了大量的经验性收益。