# Interacting Particle Markov Chain Monte Carlo

arXiv: https://arxiv.org/abs/1602.05128 · HTML: https://mlsanity.com/paper/1602.05128/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2016.

## Built on by (most cited first, 6 of 6)

- [Nested Variational Inference](https://mlsanity.com/paper/2106.11302/) (2021)
- [Interacting Contour Stochastic Gradient Langevin Dynamics](https://mlsanity.com/paper/2202.09867/) (2022)
- [Nonlinear MCMC for Bayesian Machine Learning](https://mlsanity.com/paper/2202.05621/) (2022)
- [Rethinking Variational Inference for Probabilistic Programs with Stochastic Support](https://mlsanity.com/paper/2311.00594/) (2023)
- [Thinned Mean Field Langevin Dynamics](https://mlsanity.com/paper/2605.28589/) (2026)
- [Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support](https://mlsanity.com/paper/2310.14888/) (2023)

## Abstract (arXiv)

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both non-interacting PMCMC samplers, and a single PMCMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.