Figaro : An Object-Oriented Probabilistic Programming Language

By Avi Pfeffer. 2009

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

An Introduction to Probabilistic Programming (2018, background): “, 2014), Figaro (Pfeffer, 2009), IBAL (Pfeffer, 2001)), PSI (Gehr et al.”
Design and Implementation of Probabilistic Programming Language Anglican (2016, background): “Probabilistic programming languages are implemented as interpreters [6, 9], embedded languages [5, 18, 21], and compiled languages [10, 23], and also through source-to-source transformation with augmentation [7, 29].”
Automatic Differentiation Variational Inference (2016, background): “With support for discrete latent variables, modified versions of ADVI could be extended to more general probabilistic programming systems, such as Church (Goodman et al., 2008), Figaro (Pfeffer, 2009), Venture (Mansinghka et al., 2014), and Anglican (Wood et al., 2014).”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “IBAL (Pfeffer, 2001), an ancestor of Figaro based on an embedding of stochastic choice into the functional language ML, is arguably more similar to Venture in terms of its interface.”
Generating Efficient MCMC Kernels from Probabilistic Programs (2014, result): “The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government.”
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching (2015, methodology): “Many different PPL systems have been proposed, such as BLOG [2], Figaro [3], Church [4], Venture [5], Anglican [6], and Stan [7].”
Deep Probabilistic Programming (2017, background): “On one side, there are languages which emphasize expressiveness (Pfeffer, 2001; Milch et al., 2005; Pfeffer, 2009; Goodman et al., 2012), representing a rich class beyond graphical models.”
Programming the world of uncertain things (keynote) (2016, background): “While the probabilistic programming community has built excellent semantic and theoretical foundations for creating probabilistic models for estimates (Minka et al. 2012; Goodman et al. 2008; Kiselyov and Shan 2009; Pfeffer 2009), their programming languages are not suitable for most developers.”
MXFusion: A Modular Deep Probabilistic Programming Library (2018, background): “Probabilistic programming language (PPL, Lunn et al., 2000; Pfeffer, 2001; Goodman et al., 2008; Pfeffer, 2009; Murray, 2013; Paige & Wood, 2014; Minka et al., 2014; Carpenter et al., 2017; Ge et al., 2018) is a similar domain specific programming language that aims at describing probabilistic…”
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs (2009, background): “The very recent language Figaro [29] is also implemented as a library.”
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, background): “, 2016), and unrestricted (universal), which allow arbitrary models at the expense of making inference less efficient (Goodman et al., 2008; Mansinghka et al., 2014; Wood et al., 2014; Pfeffer, 2009; Goodman and Stuhlmüller, 2014; Bingham et al., 2018).”
Augur: Data-Parallel Probabilistic Modeling (2014, background): “The regression datasets tend to be quite small in terms of both number of random variables and number of datapoints, so it is harder to amortize the costs of GPU execution.”
Turing: A Language for Flexible Probabilistic Inference (2018, background): “…programming languages [Goodman et al., 2008, Wood et al., 2014, Lunn et al., 2000, Minka et al., 2014, Stan Development Team, 2014, Murray, 2013, Pfeffer, 2001, 2009, Paige and Wood, 2014, Murray et al., 2017] aim to fill this gap by providing a very flexible framework for defining…”
Practical probabilistic programming with monads (2015, background): “Pfeffer (2009) describes a probabilistic object-oriented programming language called Figaro, which builds probabilistic programs as data structures in Scala.”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “…languages include IBAL (Pfeffer, 2001), BLOG (Milch et al., 2005), Markov logic (Richardson and Domingos, 2006), ProbLog (De Raedt et al., 2007), Church (Goodman et al., 2008), Figaro (Pfeffer, 2009), FACTORIE (McCallum et al., 2009), Anglican (Wood et al., 2014), and Edward (Tran et al., 2016).”
Probabilistic (logic) programming concepts (2015, background): “2008) and Figaro (Pfeffer 2009) and to some extent also to statistical relational learning models such as relational Bayesian networks (RBNs) (Jaeger 1997, 2008), probabilistic relational models (PRMs) (Koller and Pfeffer 1998; Getoor et al.”
Probabilistic programming with programmable inference (2018, methodology): “For example, LibBi [34] and Gen [27] support user-space definition of custom proposal distributions, and Figaro [35], Edward [49], and Pyro [1] support additional constructs.”