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MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement
Conference proceeding   Open access   Peer reviewed

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

Stephen H Muggleton, Dianhuan Lin, Jianzhong Chen and Alireza Tamaddoni-Nezhad
Inductive Logic Programming, Vol.8812, pp.1-17
Lecture Notes in Computer Science
ILP: International Conference on Inductive Logic Programming, 23 (Rio de Janeiro, Brazil, 28/08/2013–30/08/2013)
24/09/2014

Abstract

Hypothesis Space ProbLog Program Stochastic Refinement Stochastic Logic Programs (SLP) Meta-interpretive Learning (MIL)
Recent papers have demonstrated that both predicate invention and the learning of recursion can be efficiently implemented by way of abduction with respect to a meta-interpreter. 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. The resulting \documentclass[12pt]{minimal}
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https://doi.org/10.1007/978-3-662-44923-3_1View
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