PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework

By Max Kochurov, Colin Carroll, Thomas V. Wiecki, Junpeng Lao. 2019

4 citations recorded by the source corpus.

Subfields: 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 Probabilistic & Bayesian Methods

1. Yuhuai Wu
2. Jacob Austin
3. Jascha Narain Sohl-Dickstein
4. Raphael Gontijo Lopes
5. Rif A. Saurous
6. Charles Sutton
7. Diederik P. Kingma
8. Stefano Ermon

Citations in the corpus

• Language Model Cascades (2022, methodology): “We implement cascades as a trace-based probabilistic programming language embedded in Python via effect handlers, inspired by Bingham et al. (2018); Phan et al. (2019), and via coroutines, inspired by Kochurov et al. (2019).”