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 …

The mean of an unknown variance-$\sigma^ 2$ distribution $ f $ can be estimated from $ n $
samples with variance $\frac {\sigma^ 2}{n} $ and nearly corresponding subgaussian rate …

We provide an efficient unified plug-in approach for estimating symmetric properties of
distributions given $ n $ independent samples. Our estimator is based on profile-maximum …

The profile of a sample is the multiset of its symbol frequencies. We show that for samples of
discrete distributions, profile entropy is a fundamental measure unifying the concepts of …

Maximum likelihood (ML) is one of the most fundamental and general statistical estimation
techniques. Inspired by recent advances in estimating distribution functionals, we propose …

Given iid samples from an unknown distribution, estimating its symmetric properties is a
classical problem in information theory, statistics, operations research and computer …

Modern data science calls for statistical inference algorithms that are both data-efficient and
computation-efficient. We design and analyze methods that 1) outperform existing …