Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

By David Tolpin, Jan-Willem van de Meent, Brooks Paige, Frank D. Wood. 2015

11 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, 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. Yura N. Perov

In Management Science and Operations Research

1. Yura N. Perov

In Statistics and Probability

1. Yura N. Perov

In Probabilistic & Bayesian Methods

1. Yura N. Perov
2. Frank D. Wood
3. Hongseok Yang
4. Jan-Willem van de Meent
5. Vikash K. Mansinghka

Citations in the corpus

• Applications of Probabilistic Programming (Master's thesis, 2015) (2016, methodology): “…(Perov and Mansinghka, 2012)), by an intermediate compilation instead of a continuous interpretation (e.g. the recent Anglican implementation (Tolpin et al., 2015b), with a program compilation into a Clojure function, works faster than the previous Anglican interpreter (Wood et al., 2014)),…”