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A key limitation of sampling algorithms for approximate inference is that itis difficult to quantify their approximation error. Widely used samplingschemes, such as sequential importance sampling with resampling andMetropolis-Hastings, produce output samples drawn from a distribution that maybe far from the target posterior distribution. This paper shows how toupper-bound the symmetric KL divergence between the output distribution of abroad class of sequential Monte Carlo (SMC) samplers and their target posteriordistributions, subject to assumptions about the accuracy of a separategold-standard sampler. The proposed method applies to samplers that combinemultiple particles, multinomial resampling, and rejuvenation kernels. Theexperiments show the technique being used to estimate bounds on the divergenceof SMC samplers for posterior inference in a Bayesian linear regression modeland a Dirichlet process mixture model.

Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming