Syllabus

My schedule

Homework list

382 book

R stuff
A longer R intro

Book website

Week | M | W | F |

1 | 1.1 - 1.6 Probability review Hw1 | Probability review: conditional | Probability review Hw2 |

2 | 2: Random Variables | RV continued: transformations, CDF, joint distributions | RV continued |

3 | Labor Day | 3: Expected values | Expected values Hw3 |

4 | Expected values: MGFs | Chebyshev inequality and Ch.5 | 5: Convergence of RVs Hw4 |

5 | Ch.5: Theorem 5.5; Proof of CLT | Ch.5: S^2, proof of LLN | Ch.5: Delta method Hw5 |

6 | Estimates, T-distribution handout | Properties of estimates: Ch.6 | Ch.7: empirical CDF |

7 | Ch 7: plug-in estimates | Review key | Midterm Exam key |

8 | Ch 8: bootstrap (See R demo 8 under R stuff) | Ch.9: MOM Hw6 | 49ers holiday |

9 | Ch 9: Max. Likelihood | Ch.9: MLE properties | Ch.9: Fisher information Hw7 |

10 | Ch 9: F.I. examples (see R code for Cauchy) | Ch.9: Multivariate Normal and multipar. Fisher information | Ch.9: multiparameter Fisher information, Delta-method Hw8 |

11 | Ch.9: Multiparameter Delta-method | Ch.9: Gamma example (see R code ) | Ch.10: Hypothesis testing |

12 | Ch.10: Wald test Hw9 | Ch.10: Chi-square test (see Math382 textbook, Chapter 10.) | Ch.10: LR test handout |

13 | Ch.10: LR test: multiparameter | Review for Exam 2 practice key | Exam 2 key |

14 | Ch.12: Bayesian inference | Ch.12: Bayesian inference Hw10 | Thanksgiving break |

15 | Ch.12: Bayesian inference | Ch.12: Bayesian inference | Ch.24: Gibbs sampler Hw11 |

16 | Ch.24: Gibbs sampler cont'd (see R code ) | Ch.13: Linear regression | Ch.13: Linear regression Final Practice key |

Final week office hours: M,W 1-3pm

Maintained by Oleg Makhnin (olegm att nmt edu)