Practical Probabilistic Programming

By Avi Pfeffer. 2016

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Probabilistic & Bayesian Methods, Signal Processing

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. Zoubin Ghahramani
3. Hongseok Yang
4. Yoshua Bengio
5. Brooks Paige
6. Sergey Levine
7. Jan-Willem van de Meent
8. Quoc V. Le

In Computer Networks and Communications

1. Daniel Lundén
2. A. Gordon
3. Jan Kudlicka
4. David Broman
5. Zoubin Ghahramani
6. Thomas Bo Schön
7. David William Aha
8. Lawrence M. Murray

In Probabilistic & Bayesian Methods

1. Frank D. Wood
2. Hongseok Yang
3. Brooks Paige
4. Jan-Willem van de Meent
5. Zoubin Ghahramani
6. Yura N. Perov
7. A. Gordon
8. Thomas Bo Schön

In Signal Processing

1. Frank D. Wood
2. Brooks Paige
3. Zoubin Ghahramani
4. Hongseok Yang
5. Daniel Lundén
6. Jan Kudlicka
7. David Broman
8. Thomas Bo Schön

Citations in the corpus

• An Introduction to Probabilistic Programming (2018, background): “In the space of probabilistic programming arguably the recent books by Davidson-Pilon (2015) and Pfeffer (2016) are the best current references.”
• DARPA's Explainable Artificial Intelligence (XAI) Program (2019, background): “CAMEL includes a causal probabilistic programming framework that combines representations and learning methods from causal modeling (Marazopoulou et al. 2015) with probabilistic programming languages (Pfeffer 2016) to describe complex and rich phenomena.”
• Probabilistic Logical Inference on the Web (2016, background): “Probabilistic Programming (PP) [11] has recently emerged as a useful tool for building complex probabilistic models and for performing inference and learning on them.”
• Measure Transformer Semantics for Bayesian Machine Learning (2011, background): “, J. MARGETSON, AND J. VAN GAEL¨ witness the following list of probabilistic programming languages: AutoBayes [50], Alchemy [11], Blaise [7], BLOG [36], Church [15], Csoft [52], FACTORIE [32], Figaro [44], HANSEI [24], HBC [10], IBAL [42], λ◦[41], Probabilistic cc [18], PFP [12], and Probabilistic S”
• Probabilistic machine learning and artificial intelligence (2015, methodology): “The basic idea in probabilistic programming it to use computer programs to represent probabilistic models2[45, 46, 47].”
• Learning to Discover Efficient Mathematical Identities (2014, background): “The problem addressed in this paper overlaps with the areas of theorem proving [5, 9, 11], program induction [18, 28] and probabilistic programming [12, 20].”
• Distance geometry and data science (2019, background): “Moreover, although we do not review these here, there are MP-based methodologies for ensuring robustness of solutions [24], probabilistic constraints [153], and scenario-based stochasticity [28], which can be applied to the formulations in this section.”
• Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs (2017, methodology): “This work focuses on Sequential Monte Carlo (SMC) inference algorithms [4], extending an arc of research that includes probabilistic programming languages (PPLs) such as Venture [14], Anglican [26], Probabilistic C [18], WebPPL [8], Figaro [20], and Turing [7], as well as similarly-motivated softwar”