[Dmbu-l] Fwd: Spring Math/Stat Topics Course on Networks

Crovella, Mark E crovella at bu.edu
Thu Nov 10 13:08:22 EST 2016

FYI - Looks interesting - Mark

Begin forwarded message:

From: Daniel Lewis Sussman <sussman at bu.edu<mailto:sussman at bu.edu>>
Subject: Spring Math/Stat Topics Course on Networks
Date: November 10, 2016 at 12:56:21 PM EST
To: <all at math.bu.edu<mailto:all at math.bu.edu>>, <cise at bu.edu<mailto:cise at bu.edu>>, <crovella at bu.edu<mailto:crovella at bu.edu>>, <srv at bu.edu<mailto:srv at bu.edu>>
Cc: "Kolaczyk, Eric D" <kolaczyk at bu.edu<mailto:kolaczyk at bu.edu>>

Hi All,

I am writing to announce and advertise my special topics course on statistics for network data being offered Spring 2017.

Please see the description below and please forward to anyone who you think might be interested.

Dan Sussman

Statistical Modeling and Inference for Network Data

Time: Tuesdays and Thursdays, 9:30 – 11:00 am.

Website: To appear http://math.bu.edu/people/sussman/teaching.html

Course Description

Network data is becomingly increasingly ubiquitous in fields ranging from neuroscience and computational biology to the social sciences and public health. In this course we will investigate the statistical and mathematical underpinnings of analyzing these networks. We will investigate a range of statistical models for networks and corresponding statistical methods. Significant time will be spent on the stochastic blockmodel, including the limits of detectability and estimation in this model, and recent advances will be the main focus.

The special topics of the class will be partly driven by student interest. Students will lead regular discussion of journal articles related to these topics. Students who have taken GRS MA 703 Statistical Analysis of Network Data can expect some overlap but MA 882 will focus more on recent advances and theory. Students are expected to have a good knowledge of statistics at the CAS MA 575 or GRS MA 681 level. GRS MA 703 is not a prerequisite.


  *   Modeling Networks
  *   Sparsity in Network
  *   Community Detection
  *   Spectral Methods
  *   Estimation for Latent Position Models

Possible special topics include:

  *   Multiple networks and/or dynamic networks
  *   Multiplex networks
  *   Edge exchangeable networks
  *   Causal Inference for networks
  *   Applications such as neuroscience, social science, etc.


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