Applied Statistics

Syllabus
R

How to install R: Visit R Homepage and download R from a mirror site.

l R Studio (Free): a set of integrated tools for R. Very convenient.

“Bayesian Computation With R” by Jim Albert (pdf is available at University)

l Website for the book

l R scripts for examples in Second Edition (backup copy)

How to install “LearnBayes” library

l Start R

l Packages -> Install package(s)…

l Choose CRAN mirror sites: “Japan (Tokyo)[https]” and click “OK”

l Choose Packages: “LearnBayes” and click “OK”

WinBUGS

How to install WinBUGS
Examples

OpenBUGS

Available for both Windows and Mac users
Overview
Download

STATA

Bayesian analysis

Table

Probability Distributions

Supplementary Slides

Schedule in 2019 (A1A2 term)

Lecture 1 (September 27) Chapter 1 (Introduction to R) 1.2, 1.3

1.2 Summary statistics, barplots, boxplots, histogram, scatterplots
1.3 Comparison of two means, Generation of normal random variables

Lecture 2 (October 4) Chapter 1 (Introduction to R) 1.3, 2.1

1.3 How to define functions, How to input data, Monte Carlo simulation for normal and exponential populations
2.1 Introduction to Bayes Theorem

Lecture 3 (October 11) Chapter 2 (Introduction to Bayesian Statistics) 2.2, 2.3

2.2, 2.3 Binomial distribution and Discrete prior
2.4 Binomial distribution and Beta prior

Lecture 4 (October 18) Chapter 2 (Introduction to Bayesian Statistics) 2.4, 2.6

2.4 Binomial distribution and Beta prior
2.6 Prediction. Predictive density
Assignment #1

October 25 No class
Lecture 5 (November 1) Chapter 3 (Single parameter model) 3.2, 3.3

3.2 Normal distribution
3.3 Heart transplant mortality rate (Poisson distribution)

Lecture 6 (November 8) Chapter 3, 4 (Multi-parameter model) 3.3, 4.2, 4.3

3.3 Heart transplant mortality rate (Poisson distribution)
4.2 Normal data
4.3 Multinomial model
Assignment #1 submission due date
Assignment #2　election20161105.csv

November 15 No class
November 22 No class
Lecture 7 (November 29) Chapter 4, 5 (Introduction to Bayesian computation) 5.3,5.5

4.3 Multinomial model
5.3 Log posterior function in R
5.5 Approximations Based on Posterior Modes

Lecture 8 (December 6) Chapter 5, 6 (Markov chain Monte Carlo) 6.2, 6.3

5.5 Approximations Based on Posterior Modes
5.2, 5.7 Monte Carlo Integration
6.2 Markov chain
6.3 Metropolis-Hastings algorithm

December 13 Class cancelled
Lecture 9 (December 20) Chapter 6 (Markov chain Monte Carlo) 6.3, 6.4, 6.5

6.3 Metropolis-Hastings algorithm
6.4 Gibbs sampler
6.5 Output analysis

Lecture 10 (December 24) Chapter 6 (Markov chain Monte Carlo) 6.7, 6.8

Example using WinBUGS (6.7) (6.8)
Normal distributions (4.2)
Poisson distributions (3.3)
Assignment #2 submission due date
Empirical paper (Assignment #3 and Term paper)

Lecture 11 (December 27) Chapter 6 (Markov chain Monte Carlo) 2.4, 6.9, 6.10

Example using WinBUGS
Binomial distributions (2.4)
Cauchy distribution (6.9)
Analysis of Stanford heart transplant data (6.10)

Lecture 12 (January 10) 6.10, Chapter 7, Chapter 9, Chapter 11

Example using WinBUGS
Analysis of Stanford heart transplant data (6.10)
Hierarchical modeling (7.4,7.5-7.10)
(Regression model (9.2))
(Change point model (11.4))

February 4^th (Tue) Empirical paper submission due date