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Deep Horseshoe Gaussian Processes
Deep Gaussian processes have recently been proposed as natural objects to fit, similarly to deep neural networks, possibly complex features present in modern data samples, such as compositional structures.
Semiparametric inference using fractional posteriors
We establish a general Bernstein--von Mises theorem for approximately linear semiparametric functionals of fractional posterior distributions based on nonparametric priors.
Bayesian inference in high-dimensional models
Bayesian methods have been proposed for such problems more recently, where the prior takes care of the sparsity structure.
Bayesian Inference
Statistics Theory
Statistics Theory
