Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo

Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo

Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time. A Bayesian nonparametric model known as the Wishart process has been shown to be effective in this situation, but its inference remains highly challengin...

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Bibliographic Details
Main Authors: Hester Huijsdens, David Leeftink, Linda Geerligs, Max Hinne
Format: Article
Language:English
Published: MDPI AG 2024-08-01
Series:Entropy
Subjects:
dynamic covariance
Wishart processes
Bayesian inference
Sequential Monte Carlo
Markov Chain Monte Carlo
variational inference
Online Access:https://www.mdpi.com/1099-4300/26/8/695

Internet

https://www.mdpi.com/1099-4300/26/8/695

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