Rethinking Variational Inference for Probabilistic Programs with Stochastic Support

1 code implementation • 1 Nov 2023 • 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.

Variational Inference

Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support

1 code implementation • 23 Oct 2023 • 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.

Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently

no code implementations • pproximateinference AABI Symposium 2021 • Tim Reichelt, Adam Goliński, Luke Ong, Tom Rainforth

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.

Probabilistic Programming