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pro vyhledávání: '"Fasano, Augusto"'
Generalized linear models (GLMs) arguably represent the standard approach for statistical regression beyond the Gaussian likelihood scenario. When Bayesian formulations are employed, the general absence of a tractable posterior distribution has motiv
Externí odkaz:
http://arxiv.org/abs/2407.02128
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
Externí odkaz:
http://arxiv.org/abs/2309.01641
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
Externí odkaz:
http://arxiv.org/abs/2309.01630
Publikováno v:
Book of Short Papers - SIS 2023, 1133-1138
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 expectati
Externí odkaz:
http://arxiv.org/abs/2309.01619
A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including basic Gaussian response settings, this class also encompasses probit, multinomial probit
Externí odkaz:
http://arxiv.org/abs/2206.08118
Publikováno v:
Book of Short Papers - SIS 2022, 871-876
Multinomial probit (mnp) models are fundamental and widely-applied regression models for categorical data. Fasano and Durante (2022) proved that the class of unified skew-normal distributions is conjugate to several mnp sampling models. This allows t
Externí odkaz:
http://arxiv.org/abs/2206.00720
Autor:
Fasano, Augusto, Rebaudo, Giovanni
Publikováno v:
Book of Short Papers - SIS 2021 (2021) 1076-1081
Recently, Fasano, Rebaudo, Durante and Petrone (2019) provided closed-form expressions for the filtering, predictive and smoothing distributions of multivariate dynamic probit models, leveraging on unified skew-normal distribution properties. This al
Externí odkaz:
http://arxiv.org/abs/2104.07537
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Autor:
Fasano, Augusto, Durante, Daniele
Multinomial probit models are routinely-implemented representations for learning how the class probabilities of categorical response data change with p observed predictors. Although several frequentist methods have been developed for estimation, infe
Externí odkaz:
http://arxiv.org/abs/2007.06944
Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent literature, bypassing such a trade-off is still an open problem even i
Externí odkaz:
http://arxiv.org/abs/1911.06743
Publikováno v:
Statistics and Computing (2021), 31:47, 1-20
Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models - where filtering, predictive and smoothing distribut
Externí odkaz:
http://arxiv.org/abs/1902.06994