Pith. sign in

REVIEW 6 cited by

Church: a language for generative models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1206.3255 v2 pith:3GBIWC2L submitted 2012-06-13 cs.PL cs.AIcs.LO

Noah Goodman , Vikash Mansinghka , Daniel M. Roy , Keith Bonawitz , Joshua B. Tenenbaum This is my paper
classification cs.PLcs.AIcs.LO
keywords churchlanguagemodelsgenerativehistorieslispnon-parametricstochastic
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.

Discussion (0). Sign in to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 510 citations worldwide. Full citation record

  1. Expected Free Energy-based Planning as Variational Inference

    cs.AI 2026-06 unverdicted novelty 7.0 of 10

    EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.

  2. What Type of Inference is Active Inference?

    cs.AI 2026-06 unverdicted novelty 7.0 of 10

    Proper EFE-based planning is VFE plus planning and epistemic entropy corrections, realized by channel-reparameterized message passing that captures novelty.

  3. Static Factorisation of Probabilistic Programs With User-Labelled Sample Statements and While Loops

    cs.PL 2025-08 accept novelty 7.0 of 10

    Even probabilistic programs with while loops and dynamic sample labels factor into one density term per labelled sample statement, and this static factorization accelerates three Bayesian inference algorithms.

  4. Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A hybrid language-model and probabilistic-program architecture predicts human judgments on novel open-world reasoning vignettes better than language-model-only baselines.

  5. SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery

    cs.CV 2025-06 reject novelty 4.0 of 10

    SatelliteFormula couples a Swin Transformer image encoder with a symbolic regression decoder to generate expressions for indices such as NDVI and biomass from satellite imagery.

  6. Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition

    cs.AI 2025-06 reject novelty 3.0 of 10

    A formal architecture for Bayesian agents competing via a truth oracle is specified, but the main convergence result is a restatement of the assumption that utility equals truth alignment.

Pith tools