🏢 Center for Applied Statistics and School of Statistics, Renmin University of China
Nonstationary Sparse Spectral Permanental Process
·2196 words·11 mins·
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AI Generated
Machine Learning
Deep Learning
🏢 Center for Applied Statistics and School of Statistics, Renmin University of China
Nonstationary Sparse Spectral Permanental Process (NSSPP) enhances point process modeling by using sparse spectral representations, enabling flexible, efficient, nonstationary kernel learning.
Is Score Matching Suitable for Estimating Point Processes?
·1651 words·8 mins·
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AI Theory
Optimization
🏢 Center for Applied Statistics and School of Statistics, Renmin University of China
Weighted score matching offers a consistent, efficient solution for estimating parameters in point processes, overcoming the limitations of previous methods.