Commutative Semantics for Probabilistic Programming

By S. Staton. 2017

114 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Frank D. Wood
2. Hongseok Yang
3. Jan-Willem van de Meent
4. Brooks Paige
5. Yura N. Perov
6. Martin Hirzel
7. Louis Mandel
8. Avraham Shinnar

In Computational Theory and Mathematics

1. Martin Hirzel
2. Louis Mandel
3. Avraham Shinnar
4. Frank D. Wood
5. Javier Burroni
6. Brooks Paige
7. Guillaume Baudart
8. Jan-Willem van de Meent

In Probabilistic & Bayesian Methods

1. Frank D. Wood
2. Hongseok Yang
3. Jan-Willem van de Meent
4. Brooks Paige
5. Yura N. Perov
6. Martin Hirzel
7. Louis Mandel
8. Avraham Shinnar

Citations in the corpus

• An Introduction to Probabilistic Programming (2018, background): “(2016) also noticed that the posterior distribution fails to be defined in some models with certain observation distributions because the marginal likelihood of the observation can become infinite (Staton et al., 2016; Staton, 2017).”
• Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming (2021, methodology): “Compared to the language defined in [30], we added Stanlike loops.”