Expectation Propagation for the Smoothing Distribution in Dynamic Probit

Niccoló Anceschi, Augusto Fasano*, Giovanni Rebaudo

*Corresponding author

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationBayesian Statistics, New Generations New Approaches
Pages105-115
Number of pages11
DOIs
Publication statusPublished - 2023

Publication series

NameSPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS

Keywords

  • Dynamic probit model
  • Expectation propagation
  • Smoothing
  • State-space model
  • Unified skew-normal distribution

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