# A New Approach to Probabilistic Programming Inference

arXiv: https://arxiv.org/abs/1507.00996 · HTML: https://mlsanity.com/paper/1507.00996/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2015.

## Built on by (most cited first, 22 of 22)

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- [Nested Variational Inference](https://mlsanity.com/paper/2106.11302/) (2021)
- [Designing Perceptual Puzzles by Differentiating Probabilistic Programs](https://mlsanity.com/paper/2204.12301/) (2022)
- [Learning Proposals for Probabilistic Programs with Inference Combinators](https://mlsanity.com/paper/2103.00668/) (2021)
- [Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference](https://mlsanity.com/paper/2208.10530/) (2022)
- [Declarative Probabilistic Logic Programming in Discrete-Continuous Domains](https://mlsanity.com/paper/2302.10674/) (2023)
- [Nonparametric Hamiltonian Monte Carlo](https://mlsanity.com/paper/2106.10238/) (2021)
- [Automatically Marginalized MCMC in Probabilistic Programming](https://mlsanity.com/paper/2302.00564/) (2023)
- [Heterogeneous Multi-Task Gaussian Cox Processes](https://mlsanity.com/paper/2308.15364/) (2023)
- [Querying Labelled Data with Scenario Programs for Sim-to-Real Validation](https://mlsanity.com/paper/2112.00206/) (2021)
- [Rethinking Variational Inference for Probabilistic Programs with Stochastic Support](https://mlsanity.com/paper/2311.00594/) (2023)
- [Meta-Learning an Inference Algorithm for Probabilistic Programs](https://mlsanity.com/paper/2103.00737/) (2021)
- [Nonparametric Involutive Markov Chain Monte Carlo](https://mlsanity.com/paper/2211.01100/) (2022)
- [AlphaFold's Bayesian Roots in Probability Kinematics](https://mlsanity.com/paper/2505.19763/) (2025)
- [Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I](https://mlsanity.com/paper/2601.22441/) (2026)
- [Simulation-based parameter estimation via a combination of embedded normalizing flows and implied empirical probabilities under moment restrictions](https://mlsanity.com/paper/2607.25026/) (2026)
- [Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently](https://mlsanity.com/paper/2106.04953/) (2021)
- [Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming](https://mlsanity.com/paper/2208.12789/) (2022)
- [Push: Concurrent Probabilistic Programming for Bayesian Deep Learning](https://mlsanity.com/paper/2306.06528/) (2023)
- [Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support](https://mlsanity.com/paper/2310.14888/) (2023)
- [Efficient Incremental Belief Updates Using Weighted Virtual Observations](https://mlsanity.com/paper/2402.06940/) (2024)
- [LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs](https://mlsanity.com/paper/2608.08588/) (2026)

## Abstract (arXiv)

We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy to parallelize. It applies to Turing-complete probabilistic programming languages and supports accurate inference in models that make use of complex control flow, including stochastic recursion. It also includes primitives from Bayesian nonparametric statistics. Our experiments show that this approach can be more efficient than previously introduced single-site Metropolis-Hastings methods.