# Expectation propagation for the smoothing distribution in dynamic probit

arXiv: https://arxiv.org/abs/2309.01641 · HTML: https://mlsanity.com/paper/2309.01641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data](https://mlsanity.com/paper/1412.4869/) (2014)
- [Conjugate Bayes for probit regression via unified skew-normal distributions](https://mlsanity.com/paper/1802.09565/) (2018)
- [Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models](https://mlsanity.com/paper/1911.06743/) (2019)
- [Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation](https://mlsanity.com/paper/1506.08640/) (2015)
- [Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results](https://mlsanity.com/paper/2206.08118/) (2022)
- [A closed-form filter for binary time series](https://mlsanity.com/paper/1902.06994/) (2019)
- [Variational Inference for the Smoothing Distribution in Dynamic Probit Models](https://mlsanity.com/paper/2104.07537/) (2021)

## Abstract (arXiv)

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.