Fast Maximum A-Posteriori Inference on Monte Carlo State Spaces
Abstract
Many important algorithms for statistical inference can be expressed as a weighted maxkernel search problem. This is the case with the Viterbi algorithm for HMMs, message construction in maximum a posteriori BP (max-BP), as well as certain particle-smoothing algorithms. Previous work has focused on reducing the cost of this procedure in discrete regular grids [4]. MonteCarlo state spaces, which are vital for highdimensional inference, cannot be handled by these techniques. We present a novel dualtree based algorithm that is appliable to a wide range of kernels and shows substantial performance gains over nave computation.
Cite
Text
Klaas et al. "Fast Maximum A-Posteriori Inference on Monte Carlo State Spaces." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.Markdown
[Klaas et al. "Fast Maximum A-Posteriori Inference on Monte Carlo State Spaces." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.](https://mlanthology.org/aistats/2005/klaas2005aistats-fast/)BibTeX
@inproceedings{klaas2005aistats-fast,
title = {{Fast Maximum A-Posteriori Inference on Monte Carlo State Spaces}},
author = {Klaas, Mike and Lang, Dustin and Freitas, Nando},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
year = {2005},
pages = {158-165},
volume = {R5},
url = {https://mlanthology.org/aistats/2005/klaas2005aistats-fast/}
}