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.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— non-parametric calibration
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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
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian