Expectation propagation for the smoothing distribution in dynamic probit
Computation
2023-09-06 v1 Machine Learning
Abstract
The smoothing distribution of dynamic probit models with Gaussian state dynamics was recently proved to belong to the unified skew-normal family. Although this is computationally tractable in small-to-moderate settings, it may become computationally impractical in higher dimensions. In this work, adapting a recent more general class of expectation propagation (EP) algorithms, we derive an efficient EP routine to perform inference for such a distribution. We show that the proposed approximation leads to accuracy gains over available approximate algorithms in a financial illustration.
Cite
@article{arxiv.2309.01641,
title = {Expectation propagation for the smoothing distribution in dynamic probit},
author = {Niccolò Anceschi and Augusto Fasano and Giovanni Rebaudo},
journal= {arXiv preprint arXiv:2309.01641},
year = {2023}
}