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Found 4 papers, 3 papers with code
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors.
Nonparametric Involutive Markov Chain Monte Carlo
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL).
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds.
Nonparametric Hamiltonian Monte Carlo
A challenging goal is to develop general purpose inference algorithms that work out-of-the-box for arbitrary programs in a universal probabilistic programming language (PPL).
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