# Nesting Probabilistic Inference

@article{Mantadelis2011NestingPI, title={Nesting Probabilistic Inference}, author={Theofrastos Mantadelis and Gerda Janssens}, journal={ArXiv}, year={2011}, volume={abs/1112.3785} }

When doing inference in ProbLog, a probabilistic extension of Prolog, we extend SLD resolution with some additional bookkeeping. This additional information is used to compute the probabilistic results for a probabilistic query. In Prolog's SLD, goals are nested very naturally. In ProbLog's SLD, nesting probabilistic queries interferes with the probabilistic bookkeeping. In order to support nested probabilistic inference we propose the notion of a parametrised ProbLog engine. Nesting becomesâ€¦Â

## 18 Citations

Nesting Probabilistic Programs

- Computer Science, MathematicsUAI
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This work formalizes the notion of nesting probabilistic programming queries and introduces a new online nested Monte Carlo estimator that makes it substantially easier to ensure conditions required for convergence are met, thereby providing a simple framework for designing statistically correct inference engines.

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This work identifies 7 Boolean subformulae patterns that can be used to re-write Boolean formulae and implements an algorithm with polynomial complexity which detects and compacts 6 of these patterns, improving knowledge compilation and consecutively the overall inference performance.

Semantics and Contextual Equivalence for Probabilistic Programs with NestedQueries and Recursion

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This work gives formal semantics to a core PPL with continuous distributions, scoring, general recursion, and nested queries, and constructs a step-indexed, biorthogonal logical-relations model that establishes that logical relatedness implies contextual equivalence.

Tabular: Probabilistic Inference from the Spreadsheet

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The primary implementation is for Microsoft Excel and relies on Infer.NET for inference, but the language can be called independently of Excel and can target alternative inference engines.

Efficient Algorithms for Prolog Based Probabilistic Logic Programming (EfficiĂ«nte algoritmen voor prolog gebaseerd probabilistisch logisch programmeren)

- Computer Science
- 2012

This thesis focuses on the extension and implementation of ProbLog, and presents several novel extensions to the ProbLog language, such as: general negation, probabilistic meta calls and ProbLog answers, as well as several novel algorithms that improve the performance of this task.

Probabilistic (logic) programming concepts

- Computer ScienceMachine Learning
- 2015

A number of core programming concepts underlying the primitives used by various probabilistic languages are identified, the execution mechanisms that they require are discussed and these are used to position and survey state-of-the-art probabilism languages and their implementation.

Using MetaProbLog and ConArg to compute Probabilistic Argumentation Frameworks

- Computer ScienceAIÂł@AI*IA
- 2018

MetaProbLog is used, a ProbLog framework where facts in a logic program are annotated by probabilities, to compute the probability of possible worlds of arguments in Probabilistic Abstract Argumentation.

Probabilistic Argumentation Frameworks with MetaProbLog and ConArg

- Computer Science2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
- 2018

MetaProbLog is used, a ProbLog framework where facts in a logic program are annotated by probabilities, to compute the probability of possible worlds of arguments in Probabilistic Abstract Argumentation.

Probabilistic Programming Concepts

- Computer ScienceArXiv
- 2013

A number of core programming concepts underlying the primitives used by various probabilistic languages are identified, the execution mechanisms that they require are discussed and these are used to position state-of-the-art probabilism languages and their implementation.

MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement

- Computer ScienceILP
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This paper shows how Meta-Interpretive Learning (MIL) can be extended to implement a Bayesian posterior distribution over the hypothesis space by treating the meta-interpreter as a Stochastic Logic Program.

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