Corpus ID: 5002800

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference

  title={Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference},
  author={Mathias Niepert and Guy Van den Broeck},
  • Mathias Niepert, Guy Van den Broeck
  • Published 2014
  • Computer Science
  • ArXiv
  • Exchangeability is a central notion in statistics and probability theory. The assumption that an infinite sequence of data points is exchangeable is at the core of Bayesian statistics. However, finite exchangeability as a statistical property that renders probabilistic inference tractable is less well-understood. We develop a theory of finite exchangeability and its relation to tractable probabilistic inference. The theory is complementary to that of independence and conditional independence… CONTINUE READING
    49 Citations

    Figures and Topics from this paper.

    Exchangeable Variable Models
    • 9
    • PDF
    Tractable Learning for Complex Probability Queries
    • 33
    • PDF
    Exploring Unknown Universes in Probabilistic Relational Models
    • T. Braun, R. Möller
    • Computer Science
    • Australasian Conference on Artificial Intelligence
    • 2019
    • 1
    • PDF
    Taming Reasoning in Temporal Probabilistic Relational Models
    A concept for the evolution of relational probabilistic belief states and the computation of their changes under optimum entropy semantics
    • 7
    New Rules for Domain Independent Lifted MAP Inference
    • 18
    • PDF


    Lifted Probabilistic Inference: An MCMC Perspective
    • 14
    • PDF
    Exchangeable Variable Models
    • 9
    • PDF
    Lifted Inference Seen from the Other Side : The Tractable Features
    • 67
    • Highly Influential
    • PDF
    Probabilistic theorem proving
    • 137
    • Highly Influential
    • PDF
    On the Complexity and Approximation of Binary Evidence in Lifted Inference
    • Guy Van den Broeck
    • Computer Science, Mathematics
    • AAAI Workshop: Statistical Relational Artificial Intelligence
    • 2013
    • 48
    • PDF
    On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference
    • 80
    • PDF
    Lifted Probabilistic Inference with Counting Formulas
    • 208
    • PDF
    Lifted Probabilistic Inference by First-Order Knowledge Compilation
    • 160
    • PDF
    Lifted Variable Elimination with Arbitrary Constraints
    • 24
    • PDF