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A New Approach to Probabilistic Programming Inference

arXiv 1507.00996 ↗First posted 3 Jul 2015stat.ML cs.AI cs.PLcited by 22

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 6; 2024: 2; 2025: 1; 2026: 3.

22 papers.

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  1. 2024Automated Statistical Model Discovery with Language Modelscited by 41
  2. 2021Nested Variational Inferencecited by 14
  3. 2022Designing Perceptual Puzzles by Differentiating Probabilistic Programscited by 12
  4. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  5. 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inferencecited by 6
  6. 2023Declarative Probabilistic Logic Programming in Discrete-Continuous Domainscited by 5
  7. 2021Nonparametric Hamiltonian Monte Carlocited by 4
  8. 2023Automatically Marginalized MCMC in Probabilistic Programmingcited by 4
  9. 2023Heterogeneous Multi-Task Gaussian Cox Processescited by 4
  10. 2021Querying Labelled Data with Scenario Programs for Sim-to-Real Validationcited by 3
  11. 2023Rethinking Variational Inference for Probabilistic Programs with Stochastic Supportcited by 2
  12. 2021Meta-Learning an Inference Algorithm for Probabilistic Programscited by 1
10 more
  1. 2022Nonparametric Involutive Markov Chain Monte Carlocited by 1
  2. 2025AlphaFold's Bayesian Roots in Probability Kinematicscited by 1
  3. 2026Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part Icited by 1
  4. 2026Simulation-based parameter estimation via a combination of embedded normalizing flows and implied empirical probabilities under moment restrictionscited by 1
  5. 2021Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficientlycited by 0
  6. 2022Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programmingcited by 0
  7. 2023Push: Concurrent Probabilistic Programming for Bayesian Deep Learningcited by 0
  8. 2023Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Supportcited by 0
  9. 2024Efficient Incremental Belief Updates Using Weighted Virtual Observationscited by 0
  10. 2026LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programscited by 0