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Title: Scalable Marginalization of Correlated Latent Variables with Applications to Learning Particle Interaction Kernels
Marginalization of latent variables or nuisance parameters is a fundamental aspect of Bayesian inference and uncertainty quantification. In this work, we focus on scalable marginalization of latent variables in modeling correlated data, such as spatio-temporal or functional observations. We first introduce Gaussian processes (GPs) for modeling correlated data and highlight the computational challenge, where the computational complexity increases cubically fast along with the number of observations. We then review the connection between the state space model and GPs with Matérn covariance for temporal inputs. The Kalman filter and Rauch-Tung-Striebel smoother were introduced as a scalable marginalization technique for computing the likelihood and making predictions of GPs without approximation. We introduce recent efforts on extending the scalable marginalization idea to the linear model of coregionalization for multivariate correlated output and spatio-temporal observations. In the final part of this work, we introduce a novel marginalization technique to estimate interaction kernels and forecast particle trajectories. The computational progress lies in the sparse representation of the inverse covariance matrix of the latent variables, then applying conjugate gradient for improving predictive accuracy with large data sets. The computational advances achieved in this work outline a wide range of applications in molecular dynamic simulation, cellular migration, and agent-based models.  more » « less
Award ID(s):
2111303
NSF-PAR ID:
10435116
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
The New England Journal of Statistics in Data Science
ISSN:
2693-7166
Page Range / eLocation ID:
1 to 15
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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