3 papers
cs.LG2023
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches t…
cs.LG2023
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
Tim Reichelt, Luke Ong, Tom Rainforth
The posterior in probabilistic programs with stochastic support decomposes as a weighted sum of the local posterior distributions associated with each possible program path. We sho…
cs.LG2021
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently
Tim Reichelt, Adam Goliński, Luke Ong +1
We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation…