arXiv (All)
open access
Compositional Information Geometry of Probabilistic Programs
We study the compositional, intensional information geometry of probabilistic programs. For a first-order probabilistic programming language with finite random variables, we construct a contravariant lax monoidal functor that, for a program of type $X \to Y$, pulls back a (degenerate) Riemannian met
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Gradual Probabilistic Lambda Calculus#1357139
- Type-Directed Discretization of Probabilistic Programs (Extended Version)#620509
- Type-Directed Discretization of Probabilistic Programs (Extended Version)#618830
- DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models#632873
- Causal Probabilistic Programming via Magmadic Do-Notation#924989
Record · ID 1343087
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive