Student will be able to:
1) Statistically model simple experiments in a solvable fashion.
2) Detect a signal from a complex family of possible signals in colored Gaussian noise.
3) Compute the expected performance of an experiment.
4) Measure a parameter using bayesian inference.
5) Have some experience in dynamic programming and state-space methods.