Expectation propagation for the smoothing distribution in dynamic probit

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

Efficient computation of predictive probabilities in probit models via expectation propagation

Binary regression models represent a popular model-based approach for binary classification. In the Bayesian framework, computational challenges in the form of the posterior distribution motivate still-ongoing fruitful research. Here, we focus on the computation of predictive probabilities in Bayesian probit models via expectation propagation (EP). Leveraging more general results in recent literature, we show that such predictive probabilities admit a closed-form expression. Improvements over state-of-the-art approaches are shown in a simulation study

Efficient expectation propagation for posterior approximation in high-dimensional probit models

Bayesian binary regression is a prosperous area of research due to the computational challenges encountered by currently available methods either for high-dimensional settings or large datasets, or both. In the present work, we focus on the expectation propagation (EP) approximation of the posterior distribution in Bayesian probit regression under a multivariate Gaussian prior distribution. Adapting more general derivations in Anceschi et al. (2023), we show how to leverage results on the extended multivariate skew-normal distribution to derive an efficient implementation of the EP routine having a per-iteration cost that scales linearly in the number of covariates. This makes EP computationally feasible also in challenging high-dimensional settings, as shown in a detailed simulation study

Qualitative assessment of contrast-enhanced ultrasound in differentiating clear cell renal cell carcinoma and oncocytoma

Background: We aimed to assess whether clear cell renal cell carcinoma (ccRCC) can be differentiated from renal oncocytoma (RO) on a contrast-enhanced ultrasound (CEUS). Methods: Between January 2021 and October 2022, we retrospectively queried and analyzed our prospectively maintained dataset. Renal mass features were scrutinized with conventional ultrasound imaging (CUS) and CEUS. All lesions were confirmed by histopathologic diagnoses after nephron-sparing surgery (NSS). A multivariable analysis was performed to identify the potential predictors of ccRCC. The area under the curve (AUC) was depicted in order to assess the diagnostic accuracy of the multivariable model. Results: A total of 126 renal masses, including 103 (81.7%) ccRCC and 23 (18.3%) RO, matched our inclusion criteria. Among these two groups, we found significant differences in terms of enhancement (homogeneous vs. heterogeneous) (p < 0.001), wash-in (fast vs. synchronous/slow) (p = 0.004), wash-out (fast vs. synchronous/slow) (p = 0.001), and rim-like enhancement (p < 0.001). On the multivariate logistic regression, heterogeneous enhancement (OR: 19.37; p = <0.001) and rim-like enhancement (OR: 3.73; p = 0.049) were independent predictors of ccRCC. Finally, these two variables had an AUC of 82.5% and 75.3%, respectively. Conclusions: Diagnostic imaging for presurgical planning is crucial in the choice of either conservative or radical management. CEUS, with its unique features, revealed its usefulness in differentiating ccRCC from RO