Spring 2019 - STAT 853 G100
Applications of Statistical Computing (4)
Class Number: 3458
Delivery Method: In Person
An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed.
- Foundations: review of Bayesian inference, finite precision arithmetic, pseudorandom number generation, computational efficiency in matrix operations.
- Low-rank approximation of data.
- Optimization: gradient and gradient-free methods/
- Monte Carlo estimation: importance sampling, Markov chain Monte Carlo, sequential Monte Carlo methods.
- Other: EM algorithm, variational Bayes approximations to posterior distributions.
- Assignments 40%
- Participation/Attendance 20%
- Project 40%
Above grading is subject to change
Numerical Analysis for Statisticians, 2nd ed. Author: Kenneth Lange. Publisher: Springer
Monte Carlo Statistical Methods, 2nd ed. Authors: Christian Robert and George Casella. Publisher: Springer
Introducing Monte Carlo Methods with R. Authors: Christian Robert and George Casella. Publisher: Springer
Graduate Studies Notes:
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