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.
Anceschi, N., Fasano, A., Rebaudo, G., Expectation Propagation for the Smoothing Distribution in Dynamic Probit, in Avalos-Pacheco Alejandr, A. A., De Vito Robert, D. V. R., Maire Floria, M. F. (ed.), Bayesian Statistics, New Generations New Approaches, Springer Cham, Cham, Svizzera 2023: <<SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS>>, 105- 115. 10.1007/978-3-031-42413-7_10 [https://hdl.handle.net/10807/258354]
Expectation Propagation for the Smoothing Distribution in Dynamic Probit
Fasano, Augusto
;
2023
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.
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