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The smoothing distribution of dynamic probit models with Gaussian statedynamics was recently proved to belong to the unified skew-normal family.Although this is computationally tractable in small-to-moderate settings, itmay 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 adistribution. We show that the proposed approximation leads to accuracy gainsover available approximate algorithms in a financial illustration.

Expectation propagation for the smoothing distribution in dynamic probit