Effective Bayesian Inference for Stochastic Programs

By Daphne Koller, David A. McAllester, Avi Pfeffer. 1997

Subfields: Probabilistic & Bayesian Methods, Code Generation & Program Synthesis

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Citations in the corpus

An Introduction to Probabilistic Programming (2018, neutral): “, 2014), Probabilistic-C (Paige and Wood, 2014), webPPL (Goodman and Stuhlmüller, 2014), CPProb (Casado, 2017), (Koller et al., 1997), (Thrun, 2000); and statistics: Biips (Todeschini et al.”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “Along similar lines, extensions to the enumerative gibbs inference instruction could leverage the insights from (Koller et al., 1997).”
IBAL: A Probabilistic Rational Programming Language (2001, background): “The most direct presucrsor to IBAL is the language of [Koller et al., 1997], hereinafter referred to as KMP97.”
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs (2014, methodology): “The IBAL algorithm relies on acyclic computation graphs; this is equivalent to the requirement that the computation be evidence-finite [19]—there must only be a finite number of computations which can give rise to the observed evidence.”
Probabilistic programming (2014, background): “A variety of inference techniques have been implemented in probabilistic programming systems [5, 19, 21, 27, 31, 33, 41, 48].”
Measure Transformer Semantics for Bayesian Machine Learning (2011, methodology): “[26] proposed representing a probability distribution using first-order functional programs with discrete random choice, and proposed an inference algorithm for Bayesian networks and stochasti c context-free grammars.”
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling (2001, background): “Being slightly tangential to EM learning, Koller et al. (1997) developed a functional modeling language de ning a probability distribution over symbolic structures in which they showed \cashing" of computed results leads to e cient probability computation of singly connected Bayesian networks and…”
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].”
R2: An Efficient MCMC Sampler for Probabilistic Programs (2014, background): “Over the past several years, a variety of probabilistic programming languages and inference systems have been proposed (Gilks, Thomas, and Spiegelhalter 1994; Koller, McAllester, and Pfeffer 1997; Minka et al. 2009; Pfeffer 2007a; Goodman et al. 2008; Kok et al. 2007).”
Automatic model construction with Gaussian processes (2014, background): “From the top-down, the probabilistic programming community is developing automatic inference engines for extremely broad classes of models (Goodman et al., 2008; Koller et al., 1997; Mansinghka et al., 2014; Milch et al., 2007; Stan Development Team, 2014; Wood et al., 2014) such as the class of all”
Unifying logic and probability (2015, unknown): “Execution traces of a randomized program may vary in the new objects they generate; thus, PPLs have an openuniverse flavor.”