Bucket Renormalization for Approximate Inference [article]

Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin
2018 arXiv   pre-print
Probabilistic graphical models are a key tool in machine learning applications. Computing the partition function, i.e., normalizing constant, is a fundamental task of statistical inference but it is generally computationally intractable, leading to extensive study of approximation methods. Iterative variational methods are a popular and successful family of approaches. However, even state of the art variational methods can return poor results or fail to converge on difficult instances. In this
more » ... aper, we instead consider computing the partition function via sequential summation over variables. We develop robust approximate algorithms by combining ideas from mini-bucket elimination with tensor network and renormalization group methods from statistical physics. The resulting "convergence-free" methods show good empirical performance on both synthetic and real-world benchmark models, even for difficult instances.
arXiv:1803.05104v3 fatcat:cou3x644hvdqtgigkbzemurdbq