5 citations · 10 across the 12 of their papers we have counts for
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Scalable expectation propagation for generalized linear models
Niccolò Anceschi, Augusto Fasano, Beatrice Franzolini +1
Generalized linear models (GLMs) arguably represent the standard approach for statistical regression beyond the Gaussian likelihood scenario. When Bayesian formulations are employe…
Expectation propagation for the smoothing distribution in dynamic probit
Niccolò Anceschi, Augusto Fasano, Giovanni Rebaudo
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 t…
Efficient computation of predictive probabilities in probit models via expectation propagation
Augusto Fasano, Niccolò Anceschi, Beatrice Franzolini +1
Binary regression models represent a popular model-based approach for binary classification. In the Bayesian framework, computational challenges in the form of the posterior distri…
Efficient expectation propagation for posterior approximation in high-dimensional probit models
Augusto Fasano, Niccolò Anceschi, Beatrice Franzolini +1
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 l…
Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching
Evan Poworoznek, Niccolo Anceschi, Federico Ferrari +1
A wide class of Bayesian models involve unidentifiable random matrices that display rotational ambiguity, with the Gaussian factor model being a typical example. A rich variety of…