AISTATS 2022poster8 citations
Sensing Cox Processes via Posterior Sampling and Positive Bases
Abstract
We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity function. We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis. In this basis, the positivity constraint on the intensity function has a simple form. We show how the
BibTeX
@InProceedings{pmlr-v151-mutny22a,
title = { Sensing Cox Processes via Posterior Sampling and Positive Bases },
author = {Mutny, Mojmir and Krause, Andreas},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {6968--6989},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/mutny22a/mutny22a.pdf},
url = {https://proceedings.mlr.press/v151/mutny22a.html},
abstract = { We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of the intensity function and learning level sets of the intensity function. We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis. In this basis, the positivity constraint on the intensity function has a simple form. We show how the