Maximum likelihood principle, Bayesian estimation, properties of estimators, sufficiency, likelihood ratio tests, chi-square distribution, t distribution, F distribution, power, nonparametrics, bootstrap, and Markov Chain Monte Carlo.

Prerequisites:

• [MATH-256] Introduction to Probability and Statistics -- with a grade of at least C.
• [MATH-311] Probability Theory -- with a grade of at least C.

Teaching history:

• 2020 Spring

  -- G. Sokolov.
• 2018 Spring

  -- G. Sokolov.
• 2016 Spring

  -- A. Schwab-McCoy.
• 2014 Spring

  -- G. Malla.