IBAL: A Probabilistic Rational Programming Language

By Avi Pfeffer. 2001

202 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

Citations in the corpus

An Introduction to Probabilistic Programming (2018, background): “, 2014), Figaro (Pfeffer, 2009), IBAL (Pfeffer, 2001)), PSI (Gehr et al.”
Church: a language for generative models (2008, neutral): “The stochastic functional language IBAL [14], based on the functional language ML, is quite similar to Church, but the two languages emphasize different aspects of functional programming.”
Automated Variational Inference in Probabilistic Programming (2013, methodology): “Examples include BLOG [1], PRISM [2], Bayesian Logic Programs [3], Stochastic Logic Programs [4], Independent Choice Logic [5], IBAL [6], Probabilistic Scheme [7], Λ ◦ [8], Church [9], Stochastic Matlab [10], and HANSEI [11].”
A New Approach to Probabilistic Programming Inference (2014, background): “Other related e↵orts eschew Turing-completeness and operate on a restricted set of models [11, 9, 7, 10] where inference techniques other than sampling can more readily be employed.”
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.”
A Compilation Target for Probabilistic Programming Languages (2014, background): “Note that in this paper we do not show how to compile any existing probabilistic programming languages (i.e. IBAL (Pfeffer, 2001), BLOG (Milch et al., 2007), Church (Goodman et al., 2008), Figaro (Pfef-fer, 2009), Venture (Mansinghka et al., 2013), or Anglican (Wood et al., 2014)) to this intermedia”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “Examples of probabilistic programming languages include IBAL (Pfeffer, 2001), BLOG (Milch et al.”
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.”
Unifying logic and probability (2015, unknown): “The first significant PPL was Pfeffer’s IBAL [27], a functional language equipped with an effective inference engine.”
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…”
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs (2014, background): “The most closely related system to the present work is the functional programming language IBAL, a probabilistic variant of ML [25].”
Probabilistic (logic) programming concepts (2015, methodology): “At the same time, we also relate the concepts to alternative probabilistic programming languages such as IBAL (Pfeﬀer, 2001), Bayesian logic (BLOG) (Milch et al, 2005), Church (Goodman et al, 2008) and Figaro (Pfeﬀer, 2009) and to some extent also to statistical relational learning models such as re”
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, background): “Although some of the early formalisms [Poole, 1993b, Sato, 1995, Pfeffer, 2001] already extend an existing programming language with probabilities, and hence, possess the power of a universal Turing machine, this stream of research became popular since BLOG [Milch et al., 2005] and Church [Goodman…”
Applications of Probabilistic Programming (Master's thesis, 2015) (2016, background): “Other languages and implementations also endorse declarative definitions of probabilistic models and include IBAL (Pfeffer, 2001), 4 Stan (Stan Development Team, 2014), BLOG (Milch et al., 2007), BUGS (Lunn et al., 2009), FACTORIE (McCallum et al., 2009), Markov Logic networks (Richardson and Doming”
BLOG: Probabilistic Models with Unknown Objects (2005, methodology): “Finally, it is informative to compare B LOG with the IBAL language [Pfeffer, 2001], in which a program deﬁnes a distribution over outputs that can be arbitrary nested data structures.”
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation (2011, methodology): “Examples include BLOG [7], PRISM [15], Bayesian Logic Programs [5] Stochastic Logic Programs [9], Markov Logic [14], Independent Choice Logic [12], IBAL [11], Proba-bilistic Scheme [13], § ± [10], and HANSEI [6].”
PSI: Exact Symbolic Inference for Probabilistic Programs (2016, methodology): “The general approaches include sampling-based Monte Carlo methods [20,40,21,19,39,46,32] and projections to convenient probability distributions, such as variational inference [34,8] or discretization [31,12].”
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs (2009, methodology): “Unlike IDFs, BLOG, as well as IBAL [6] and Church [7], are designed for generative models, though Church can also represent conditional, undirected models.”