Probabilistic Inference by Program Transformation in Hakaru (System Description)

By P. Narayanan, J. Carette, Wren Romano, Chung-chieh Shan, R. Zinkov. 2016

139 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, 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. S. Staton
7. Vikash K. Mansinghka
8. Dustin Tran

In Computer Networks and Communications

1. Frank D. Wood
2. Jan-Willem van de Meent
3. Hongseok Yang
4. Brooks Paige
5. Vikash K. Mansinghka
6. Mark van der Wilk
7. Danijar Hafner
8. Noah D. Goodman

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. S. Staton
7. Vikash K. Mansinghka
8. Gwonsoo Che

Citations in the corpus

• Foundation Posteriors for Approximate Probabilistic Inference (2022, neutral): “Many probabilistic programming languages (PPLs) have been developed [27, 45, 28, 44, 18, 58, 47, 53, 59, 12, 23, 17, 7, 57] each with inference approaches with strengths and weaknesses.”
• An Introduction to Probabilistic Programming (2018, background): “Some probabilistic programming systems operate in exactly this way (Narayanan et al., 2016).”
• Commutative Semantics for Probabilistic Programming (2017, background): “Our language syntax is not novel: it is the same language as in [43], and as such an idealized, typed, first order version of Anglican [46], Church [11], Hakaru [30], Venture [26] and so on.”
• Gen: a general-purpose probabilistic programming system with programmable inference (2019, background): “Systems that take this approach include Birch [45], which exploits conjugate prior relationships between model variables, and Hakaru [46], which can simplify algebraic expressions and symbolically disintegrate some models.”
• Bayesian Layers: A Module for Neural Network Uncertainty (2018, methodology): “Examples include HANSEI on OCaml, Church on Lisp, and Hakaru on Haskell (Kiselyov and Shan, 2009; Goodman et al., 2012; Narayanan et al., 2016).”
• Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, background): “This line of research has led to performant probabilistic programming systems (Goodman et al., 2008; Wood et al., 2014; Mansinghka et al., 2014; Minka et al., 2018; Narayanan et al., 2016; Salvatier et al., 2016; Carpenter et al., 2017; Tran et al., 2016; Ge et al., 2018; Bingham et al., 2018).”