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Feb 17, 2026
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STAT 54600 - Computational Statistics
Credit Hours: 3.00. The course focuses on two fundamental aspects in computational statistics: (1) what to compute and (2) how to compute. The first is covered with a brief review of advanced topics in statistical inference, including Fisher’s fiducial inference, Bayesian and frequenstist methods, and the Dempster-Shafer (DS) Theory. The second is discussed in detail by examining exact, approximation, and interactive simulation methods for statistical inference with a variety of commonly used statistical models. The emphasis is on the EM-type and quasi-Newton algorithms, numerical differentiation and integration, and Markov chain Monte Carlo methods.
Learning Outcomes
1. Master commonly used numerical matrix operations, including the sweep operator, Cholesky decomposition, eigenvalue decomposition, and single value decomposition.
2. Understand statistical thinking in development of interactive numerical methods.
3. Apply optimization algorithms such as quasi-Newton and conjugate gradient methods.
4. Implement EM-type algorithms for maximum likelihood estimation when expanded complete-data models are available, including commonly used statistical models.
5. Create random number generators.
6. Implement the Gibbs sampler and Metropolis-Hastings algorithms for Bayesian data analysis.
Credits: 3.00
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