# STA 410/2102 - Topics for Test 2

Here are the topics to study for for Test 2. Unfortunately, I haven't
been able to find the time to produce actual study questions.
- Basic concepts of maximum likelihood estimation (no proofs required).
- Getting standard errors from the expected (Fisher) information matrix or
the observed information matrix.
- Finding maximum likelihood estimates using Newton-Raphson iteration
or the Method of Scoring, for single parameter and multi-parameter
problems.
- Convergence of Newton-Raphson iteration and other optimization methods,
including the meaning of linear and quadratic convergence rates.
- Basic concepts of Bayesian inference.
- Integration by trapezoid, Simpson's, and midpoint rules.
- Rates of convergence of these methods.