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Corcoran, Jem Associate Professor

Positions

Research Areas research areas

Research

research overview

  • Dr. Corcoran's research is focused on fast accurate Markov chain Monte Carlo (MCMC) algorithms with applications to problems in high-dimensional Bayesian network inference, target tracking, statistical mechanics, high-energy physics, and rarefied gas dynamics. Most recently, she has been involved in real time detection of rapid changes in synchronous flocking behavior and the development of perfect simulation algorithms for finding equilibriums in chemical kinetic networks.

keywords

  • Monte Carlo algorithms, Multi-Target Tracking, Recovering Bayesian Networks from Data and from Discretized Data, Particle Filtering , Direct Simulation Monte Carlo for Rarefied Gas Dynamics

Publications

selected publications

Teaching

courses taught

  • APPM 4520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2019
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as STAT 5520 and MATH 4520 and MATH 5520.
  • APPM 4560 - Markov Processes, Queues, and Monte Carlo Simulations
    Primary Instructor - Spring 2019 / Spring 2020
    Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time, including Poisson point processes. Queuing theory, terminology and single queue systems are studied with some introduction to networks of queues. Uses Monte Carlo simulation of random variables throughout the semester to gain insight into the processes under study. Same as APPM 5560 and STAT 4100.
  • APPM 4720 - Open Topics in Applied Mathematics
    Primary Instructor - Spring 2018
    Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 15 total credit hours. Same as APPM 5720.
  • APPM 5520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2019
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: one semester calculus-based probability course, such as MATH 4510 or APPM 3570. Same as STAT 4520 and MATH 4520 and MATH 5520.
  • APPM 5560 - Markov Processes, Queues, and Monte Carlo Simulations
    Primary Instructor - Spring 2019 / Spring 2020
    Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time, including Poisson point processes. Queuing theory, terminology and single queue systems are studied with some introduction to networks of queues. Uses Monte Carlo simulation of random variables throughout the semester to gain insight into the processes under study. Same as APPM 4560, STAT 4100 and STAT 5100.
  • APPM 5720 - Open Topics in Applied Mathematics
    Primary Instructor - Spring 2018 / Fall 2018
    Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 6 total credit hours. Same as APPM 4720.
  • APPM 7400 - Topics in Applied Mathematics
    Primary Instructor - Fall 2018
    Provides a vehicle for the development and presentation of new topics with the potential of being incorporated into the core courses in applied mathematics. May be repeated up to 6 total credit hours.
  • MATH 4520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2019
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as MATH 5520 and STAT 4520 and STAT 5520.
  • MATH 5520 - Introduction to Mathematical Statistics
    Primary Instructor - Fall 2019
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: MATH 4510 or MATH 5510 or APPM 3570. Same as MATH 4520 and STAT 4520 and STAT 5520.

Background

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