Markov Random Walk Representations with Continuous Distributions
Abstract
Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the data density. We relate this diffusion equation to a path integral and derive the corresponding path probability measure. The framework is useful for incorporating continuous data densities and prior knowledge.
Cite
Text
Yeang and Szummer. "Markov Random Walk Representations with Continuous Distributions." Conference on Uncertainty in Artificial Intelligence, 2003.Markdown
[Yeang and Szummer. "Markov Random Walk Representations with Continuous Distributions." Conference on Uncertainty in Artificial Intelligence, 2003.](https://mlanthology.org/uai/2003/yeang2003uai-markov/)BibTeX
@inproceedings{yeang2003uai-markov,
title = {{Markov Random Walk Representations with Continuous Distributions}},
author = {Yeang, Chen-Hsiang and Szummer, Martin},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2003},
pages = {600-607},
url = {https://mlanthology.org/uai/2003/yeang2003uai-markov/}
}