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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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer - non-parametric calibration
🐝 Cross-Pollinator - Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

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
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