论文标题
非参数涉及马尔可夫链蒙特卡洛
Nonparametric Involutive Markov Chain Monte Carlo
论文作者
论文摘要
概率编程中的一个具有挑战性的问题是开发用于通用概率编程语言（PPL）任意程序的推理算法。我们将非参数涉及马尔可夫链蒙特卡洛（NP-IMCMC）算法作为一种用于在通用PPLS中表达的非参数模型的MCMC推理算法的方法。在统一参与的MCMC框架的基础上，并通过提供在维度之间推动状态运动的一般程序，我们表明NP-IMCMC可以概括许多现有的IMCMC算法以在非参数模型上工作。我们证明了NP-IMCMC采样器的正确性。我们的实证研究表明，几种IMCMC算法的现有优势延续到其非参数扩展。将我们的方法应用于最近提出的非参数HMC，即（多步）NP-IMCMC的一个实例，我们构建了几个非参数扩展（所有新），这些扩展具有显着改善的性能。
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We present the nonparametric involutive Markov chain Monte Carlo (NP-iMCMC) algorithm as a method for constructing MCMC inference algorithms for nonparametric models expressible in universal PPLs. Building on the unifying involutive MCMC framework, and by providing a general procedure for driving state movement between dimensions, we show that NP-iMCMC can generalise numerous existing iMCMC algorithms to work on nonparametric models. We prove the correctness of the NP-iMCMC sampler. Our empirical study shows that the existing strengths of several iMCMC algorithms carry over to their nonparametric extensions. Applying our method to the recently proposed Nonparametric HMC, an instance of (Multiple Step) NP-iMCMC, we have constructed several nonparametric extensions (all of which new) that exhibit significant performance improvements.
