For decades, statisticians have studied the species problem: how to estimate the total
number of species, observed plus unobserved, in a population. This problem dates at least …

Discrete random probability measures and the exchangeable random partitions they induce
are key tools for addressing a variety of estimation and prediction problems in Bayesian …

Bayesian (machine) learning has been playing a significant role in machine learning for a
long time due to its particular ability to embrace uncertainty, encode prior knowledge, and …

A new class of dependent random measures which we call compound random measures is
proposed and the use of normalized versions of these random measures as priors in …

Consider a finite sample from an unknown distribution over a countable alphabet. The
occupancy probability (OP) refers to the total probability of symbols that appear exactly k …

Bayesian nonparametric mixture models are common for modeling complex data. While
these models are well‐suited for density estimation, recent results proved posterior …

Given an observed sample from a population of individuals belonging to species," species-
sampling" problems (SSPs) call for estimating some features of the unknown species …

A fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution
by assuming that the corresponding predictive probabilities obey certain properties. An early …

Consider a finite sample from an unknown distribution over a countable alphabet. The
missing mass refers to the probability of symbols that do not appear in the sample …

The definition and investigation of general classes of nonparametric priors has recently
been an active research line in Bayesian statistics. Among the various proposals, the Gibbs …