Skip to content
conftrace_
2022 AAAI AAAI 2022

Numerical Approximations of Log Gaussian Cox Process (Student Abstract)

Abstract

Abstract This paper considers a multi-state Log Gaussian Cox Process (`"LGCP'') on a graph, where transmissions amongst states are calibrated using a non-parametric approach. We thus consider multi-output LGCPs and introduce numerical approximations to compute posterior distributions extremely quickly and in a completely transparent and reproducible fashion. The model is tested on historical data and shows very good performance.

Authors

Francois Buet-Golfouse, Hans Roggeman

Topics

Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian

Keywords

nonparametric bayesian bayesian inference posterior distribution stochastic process multi-output gaussian process log gaussian cox process non-parametric calibration
Download PDF

More from AAAI 2022

All AAAI 2022 papers →
A Multimodal Fusion-Based LNG Detection for Monitoring Energy Facilities (Student Abstract)
Controlling the Spread of Two Secrets in Diverse Social Networks (Student Abstract)
Bridging the Gap between Expression and Scene Text for Referring Expression Comprehension (Student Abstract)
Thrifty Neural Architecture Search for Medical Image Segmentation (Student Abstract)
Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract)
An Emotion-Based Multi-Task Approach to Fake News Detection (Student Abstract)