Robust inference of dynamic covariance using Wishart processes and sequential Monte Carlo
Publication year
2024Number of pages
27 p.
Source
Entropy, 26, 8, (2024), article 695ISSN
Publication type
Article / Letter to editor
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Organization
SW OZ DCC AI
Journal title
Entropy
Volume
vol. 26
Issue
iss. 8
Languages used
English (eng)
Subject
Cognitive artificial intelligenceAbstract
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 challenging. In this work, we introduce a Sequential Monte Carlo (SMC) sampler for the Wishart process, and show how it compares to conventional inference approaches, namely MCMC and variational inference. Using simulations, we show that SMC sampling results in the most robust estimates and out-of-sample predictions of dynamic covariance. SMC especially outperforms the alternative approaches when using composite covariance functions with correlated parameters. We further demonstrate the practical applicability of our proposed approach on a dataset of clinical depression (n=1), and show how using an accurate representation of the posterior distribution can be used to test for dynamics in covariance.
This item appears in the following Collection(s)
- Academic publications [245103]
- Electronic publications [132420]
- Faculty of Social Sciences [30334]
- Open Access publications [106000]
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