BLOG: Probabilistic Models with Unknown Objects

By Brian Milch, Bhaskara Marthi, Stuart J. Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov. 2005

550 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

Citations in the corpus

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].”
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, methodology): “Among other strategies [Milch et al., 2005], an infinite Herbrand base would enable this, as is possible in probabilistic (logic) programming [Raedt et al.”
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (2016, background): “These models range from highly specified [17, 16], to partially specified [28, 24, 25], to largely unspecified [22].”
Church: a language for generative models (2008, methodology): “Stochastic programming languages [e.g. 12, 14, 10] aim to tame the model-building process by giving a formal language which provides simple, uniform, and re-usable descriptions of a wide class of models, and supports generic inference techniques.”
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].”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “Also, the Gibbs sampling algorithm introduced for BLOG (Arora et al., 2012) exploits a decomposition of a possible world that is related to the scaffolds used to define the scope of inference in Venture.”
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation (2011, methodology): “Our MCMC algorithm is a new implementation of the Church MCMC algorithm [4], and is similar to the algo-rithm used in BLOG [7].”
Unifying logic and probability (2015, unknown): “The BLOG language [20] provides a precise syntax, semantics, and inference capability for open-universe probability models composed of dependency and number statements.”
ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains (2015, background): “In the context of probabilistic models, we remark that there are other realizations of program-based probabilistic behavior, such as probabilistic programming [Milch et al., 2005; Goodman et al., 2008].”
Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference (2020, methodology): “The semantics of Bean Machine are very similar to those established in the BLOG paper.”
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference (2011, methodology): “Examples include BLOG [13], Bayesian Logic Programs [10] IBAL[18], CHURCH [6], Stochastic MATLAB [28], and HANSEI [11].”
Figaro : An Object-Oriented Probabilistic Programming Language (2009, background): “Some languages are logic-based, such as PRISM [10], BLOG [6] and Markov logi c [1].”
BayesDB: A probabilistic programming system for querying the probable implications of data (2015, background): “Most probabilistic programming languages are intended for model speciﬁcation (Goodman et al., 2008; Stan Development Team, 2015; Milch et al., 2007; Pfeﬀer, 2009).”
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, background): “…the set of expressible models to ensure that particular inference algorithms can be efficiently applied (Lunn et al., 2009; Minka et al., 2018; Milch et al., 2005; Carpenter et al., 2017; Tran et al., 2016), and unrestricted (universal), which allow arbitrary models at the expense of making…”
Entity Resolution with Markov Logic (2006, background): “Milch et al [29] propose a language for reasoning about entity resolution .”
Probabilistic logic programs: Unifying program trace and possible world semantics (2017, background): “This corresponds to the generative model view in programming languages such as IBAL, BLOG and distributional clauses, which is also in line with the possible world view.”
Learning overhypotheses with hierarchical Bayesian models. (2007, background): “We defined models that generate scalars (α) and vectors (β β β β, θ θ θ θ, y), but hierarchical probabilistic models can generate many other kinds of representations, including taxonomies (Kemp, Perfors & Tenenbaum, 2004), ontologies (Schmidt, Kemp & Tenenbaum, 2006), causal networks (Mansinghka, Ke”