# Particle Gibbs with Ancestor Sampling for Probabilistic Programs

arXiv: https://arxiv.org/abs/1501.06769 · HTML: https://mlsanity.com/paper/1501.06769/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [Nested Variational Inference](https://mlsanity.com/paper/2106.11302/) (2021)
- [Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming](https://mlsanity.com/paper/2208.12789/) (2022)
- [Marginalized particle Gibbs for multiple state-space models coupled through shared parameters](https://mlsanity.com/paper/2210.07379/) (2022)

## Abstract (arXiv)

Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a program. We here develop a formalism to adapt ancestor resampling, a technique that mitigates particle degeneracy, to the probabilistic programming setting. We present empirical results that demonstrate nontrivial performance gains.