Many recent invertible neural architectures are based on coupling block designs where variables divided in two subsets which serve as inputs of an easily (usually affine) triangular transformation. While such a transformation is invertible, its Jacobian very sparse and thus may lack expressiveness. This work presents simple remedy by noting that subdivision (affine) can be repeated recursively ...

We consider nonparametric Bayesian estimation inference using a rescaled smooth Gaussian field as a prior for a multidimensional function. The rescaling is achieved using a Gamma variable and the procedure can be viewed as choosing an inverse Gamma bandwidth. The procedure is studied from a frequentist perspective in three statistical settings involving replicated observations (density estimati...

We consider the inference problem of estimating covariate and genetic effects in a family-based case-control study where families are ascertained on the basis of the number of cases within the family. However, our interest lies not only in estimating the fixed covariate effects but also in estimating the random effects parameters that account for varying correlations among family members. These...

Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial “sparse” methodology. We derive a large scale approximate Bayesian inference algorithm for linear models with nonfact...

We propose a class of Bayesian nonparametric mixture models with a Beta distribution providing the mixture kernel and a Dirichlet process prior assigned to the mixing distribution. Motivating applications include density estimation on bounded domains, and inference for non-homogeneous Poisson processes over time. We present the mixture model formulation, discuss prior specification, and develop...