Abstraction Sampling in Graphical Models

Authors

  • Filjor Broka University of California, Irvine

Keywords:

graphical models, partition function, importance sampling, heuristic search

Abstract

We present a new sampling scheme for approximating hard to compute queries over graphical models, such as computing the partition function. The scheme builds upon exact algorithms that traverse a weighted directed state-space graph representing a global function over a graphical model (e.g., probability distribution). With the aid of an abstraction function and randomization, the state space can be compacted (trimmed) to facilitate tractable computation, yielding a Monte Carlo estimate that is unbiased. We present the general idea and analyze its properties analytically and empirically.

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Published

2018-04-29

How to Cite

Broka, F. (2018). Abstraction Sampling in Graphical Models. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11365