We propose a nonparametric compound Poisson model for underreported count data that
introduces a latent clustering structure for the reporting probabilities. The latter are estimated …

Topic models are learned via a statistical model of variation within document collections, but
designed to extract meaningful semantic structure. Desirable traits include the ability to …

To construct flexible nonlinear predictive distributions, the paper introduces a family of
softplus function based regression models that convolve, stack, or combine both operations …

In inverse synthetic aperture radar (ISAR) imaging, some weak scatterers might be missing
in the results obtained by conventional compressive sensing (CS)-based methods. This …

The stable under iteration (STIT) tessellation process is a stochastic process that produces a
recursive partition of space with cut directions drawn independently from a distribution over …

We present the hierarchical Dirichlet scaling process (HDSP), a Bayesian nonparametric
mixed membership model for multi-labeled data. We construct the HDSP based on the …

We consider the problem of spatially dependent areal data, where for each area
independent observations are available, and propose to model the density of each area …

The Dirichlet process mixture (DPM) model has been widely used as a Bayesian
nonparametric model for clustering. However, the exchangeability assumption of the …

Assessing the contribution of various risk factors to future inflation risks was crucial for
guiding monetary policy during the recent high inflation period. However, existing …

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain
modeling flexibility and robustness against mis-specification of the probability model. In the …