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STOR Colloquium: Mariana Olvera-Cravioto, University of California-Berkeley

March 1 @ 3:30 pm - 4:30 pm

Directed complex networks and ranking algorithms In the first part of this talk I will discuss a family of inhomogeneous directed random graphs for modeling complex networks such as the web graph, Twitter, ResearchGate, and other social networks. This class of graphs includes as a special case the classical Erdos-Renyi model, and can be used to replicate almost any type of predetermined degree distributions, in particular, power-law degrees such as those observed in most real-world networks. I will mention during…

STOR Colloquium: Zhiyi Zhang, UNC Charlotte

March 6 @ 3:30 pm - 4:30 pm

Title: Statistical Implications of Turing’s Formula   Abstract: This talk is organized into three parts.   Turing’s formula is introduced. Given an iid sample from a countable alphabet under a probability distribution, Turing’s formula (introduced by Good (1953), hence also known as the Good-Turing formula) is a mind-bending non-parametric estimator of total probability associated with letters of the alphabet that are NOT represented in the sample. Many of its statistical properties were not clearly known for a stretch of nearly…

Hotelling Lectures: Aad van der Vaart, Leiden University

March 27 @ 3:30 pm - 4:30 pm

Nonparametric Bayesian methods: frequentist analysis Aad van der Vaart Leiden University We present an overview of Bayesian methods to estimate functions or high-dimensional parameter vectors, and discuss the validity (or not) of these methods from a non-Bayesian point of view. For instance, we consider using a Gaussian process as a prior for a regression function or (after exponentiation and normalisation) for a density function. We characterise the rate at which the corresponding posterior distribution can recover a true function as…

Hotelling Lectures: Aad van der Vaart, Leiden University

March 29 @ 3:30 pm - 5:00 pm

Nonparametric Bayesian methods: frequentist analysis Aad van der Vaart Leiden University A more detailed view of Bayesian methods to estimate functions or high-dimensional parameter vectors, and discuss the validity (or not) of these methods from a non-Bayesian point of view. For instance, we consider using a Gaussian process as a prior for a regression function or (after exponentiation and normalisation) for a density function. We characterise the rate at which the corresponding posterior distribution can recover a true function as…

STOR Colloquium: Hongtu Zhu, MD Anderson

April 3 @ 3:30 pm - 4:30 pm
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