Skip to main content
Cornell university
Cornell Statistics and Data Science Cornell Statistics and Data Science
  • About Us

    About Us
    Cornell's Department of Statistics and Data Science offers four programs at the undergraduate and graduate levels. Undergraduates can earn a BA in statistical science, social...

    Welcome to the Department of Statistics and Data Science
    History
    Facilities
    Statistics Graduate Society
    Recently Published Papers
  • Academics

    Academics

    Undergraduate
    PhD
    MPS
    PhD Minor in Data Science
    Courses & Course Enrollment
  • People

    People

    Faculty
    Field Faculty
    PhDs
    Emeritus Faculty
    Academic Staff
    Staff
    Research Areas of Expertise
    Statistical Consultants
  • News and Events

    News and Events

    Events
    News
  • Resources

    Resources

    Professional Societies and Meetings
    Affiliated Groups
    Career Services
    Cornell Statistical Consulting Unit
  • Alumni

    Alumni
    Cornell's Statistics and Data Science degrees prepare students for a wide variety of careers, from academia to industry.  See the After Graduation page for a general overview of...

    Alumni Profiles

Search form

You are here

  1. Home 
  2. Events 
  3. Statistics Seminars

Statistics Seminar Speaker Jessi Cisewski, 1/21/15

Event Layout

Wednesday Jan 21 2015

Statistics Seminar Speaker Jessi Cisewski, 1/21/15

4:15pm @ G01 Biotechnology
In Statistics Seminars

The Statistics Seminar speaker for Jan. 21, 2015 will be Jessi Cisewski, from the Carnegie Mellon University. 

Title: Approximate Bayesian Computation for the Stellar Initial Mass Function

Abstract: Explicitly specifying a likelihood function is becoming increasingly difficult for many problems in astronomy.  Astronomers often specify a simpler approximate likelihood - leaving out important aspects of a more realistic model.  Estimation of a stellar initial mass function (IMF) is one such example.  The stellar IMF is the mass distribution of stars initially formed in a particular volume of space, but is typically not directly observable due to stellar evolution and other disruptions of a cluster. Several difficulties associated with specifying a realistic likelihood function for the stellar IMF will be addressed in this talk.

Approximate Bayesian computation (ABC) provides a framework for performing inference in cases where the likelihood is not available.  I will introduce ABC, and demonstrate its merit through a simplified IMF model where a likelihood function is specified and exact posteriors are available.  To aid in capturing the dependence structure of the data, a new formation model for stellar clusters using a preferential attachment framework will be presented.  The proposed formation model, along with ABC, provides a new mode of analysis of the IMF.

I will also discuss an emerging area of topological data analysis called persistent homology.   Persistent homology offers a novel way to represent, visualize, and interpret complex data by extracting topological features, which can be used to infer properties of the underlying structures.  Data exhibiting complicated spatial structures are common in many areas of science (e.g. cosmology, biology), but can be difficult to analyze. I will explain how studying the topological features could lead to a significant enhancement of our understanding of complex data structures, such as the large-scale structure of the Universe.  Finally, I will introduce a framework for hypothesis testing using the summaries from persistent homology.

Refreshments will be served after the seminar in 1181 Comstock Hall.

 

 

Event Categories

  • Statistics Seminars
  • Special Events
  • Home
  • About Us
  • Contact Us
  • Careers
© Cornell University Department of Statistics and Data Science

1198 Comstock Hall, 129 Garden Ave., Ithaca, NY 14853

Social Menu

  • Facebook
  • Twitter
  • YouTube
Cornell Bowers CIS College of Computing and Information Science Cornell CALS ILR School

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact web-accessibility@cornell.edu for assistance.