The smoothing distribution of dynamic probit models with Gaussian state
...
Binary regression models represent a popular model-based approach for bi...
Bayesian binary regression is a prosperous area of research due to the
c...
Bayesian nonparametric mixture models are widely used to cluster
observa...
Bayesian nonparametric mixtures and random partition models are powerful...
Speech sounds subtly differ on a multidimensional auditory-perceptual sp...
Multinomial probit (mnp) models are fundamental and widely-applied regre...
Dirichlet process mixtures are flexible non-parametric models, particula...
The Bayesian approach to inference stands out for naturally allowing
bor...
We argue for the use of separate exchangeability as a modeling principle...
Recently, Fasano, Rebaudo, Durante and Petrone (2019) provided closed-fo...
Non-Gaussian state-space models arise routinely in several applications....