4 papers · 1 filter
Partial Differential Equation Barriers to Identifiability in Infinite Mixture Models
Dung Le, Nicola Bariletto, Alessandro Rinaldo +1
We study identifiability of mixing measures in infinite mixture models. We show that, in many common cases, lack of identifiability can be characterized in terms of certain differe…
Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Nicola Bariletto, Dung Le, Alessandro Rinaldo +1
We study posterior contraction rates for mixing measures in homoscedastic location-scale mixture models with infinitely many components. While posterior convergence at the level of…
On A Necessary Condition For Posterior Inconsistency: New Insights From A Classic Counterexample
Nicola Bariletto, Stephen G. Walker
The consistency of posterior distributions in density estimation is at the core of Bayesian statistical theory. Classical work established sufficient conditions, typically combinin…
Posterior Consistency in Parametric Models via a Tighter Notion of Identifiability
Nicola Bariletto, Bernardo Flores, Stephen G. Walker
We study Bayesian posterior consistency in parametric density models with proper priors, challenging the perception that the problem is settled. Classical results established consi…