Views Navigation

Event Views Navigation

Calendar of Events

S Sun

M Mon

T Tue

W Wed

T Thu

F Fri

S Sat

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Statistics and Data Science Seminar Alexander Barvinok (University of Michigan)

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Statistics and Data Science Seminar Ankur Moitra (MIT)

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Statistics and Data Science Seminar David Dunson (Duke)

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

1 event,

Statistics and Data Science Seminar Shankar Bhamidi (UNC)

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

0 events,

Computing partition functions by interpolation

Alexander Barvinok (University of Michigan)
E18-304

Abstract: Partition functions are just multivariate polynomials with great many monomials enumerating combinatorial structures of a particular type and their efficient computation (approximation) are of interest for combinatorics, statistics, physics and computational complexity. I’ll present a general principle: the partition function can be efficiently approximated in a domain if it has no complex zeros in a slightly…

Find out more »

Robust Statistics, Revisited

Ankur Moitra (MIT)

Starting from the seminal works of Tukey (1960) and Huber (1964), the field of robust statistics asks: Are there estimators that provable work in the presence of noise? The trouble is that all known provably robust estimators are also hard to compute in high-dimensions. Here, we study a basic problem in robust statistics, posed in…

Find out more »

Probabilistic factorizations of big tables and networks

David Dunson (Duke)

Abstract: It is common to collect high-dimensional data that are structured as a multiway array or tensor; examples include multivariate categorical data that are organized as a contingency table, sequential data on nucleotides or animal vocalizations, and neuroscience data on brain networks. In each of these cases, there is interest in doing inference on the joint…

Find out more »

Jagers-Nerman stable age distribution theory, change point detection and power of two choices in evolving networks

Shankar Bhamidi (UNC)
E18-304

Abstract: (i) Change point detection for networks: We consider the preferential attachment model. We formulate and study the regime where the network transitions from one evolutionary scheme to another. In the large network limit we derive asymptotics for various functionals of the network including degree distribution and maximal degree. We study functional central limit theorems for…

Find out more »


MIT Statistics + Data Science Center
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139-4307
617-253-1764