STAT 764
Preface
1
August 18
1.1
Welcome
1.2
Course format
1.3
Reading Assignment
1.4
Assignment 1
2
August 20
2.1
Announcements
2.2
Statistical models
2.3
Matrix review
2.4
Distribution theory review
2.5
Mathematical model review
2.6
Summary and comments
3
August 25
3.1
Announcements
3.2
Statistical models
3.3
Bayesian hierarchical models
3.3.1
Motivating data example
3.3.2
The data model
3.3.3
The process model
3.3.4
The parameter model
3.3.5
Simulating data from the prior predictive distribution
3.3.6
Model fitting
3.4
Summary and comments
4
August 27
4.1
Announcements
4.2
Bayesian hierarchical models
4.2.1
Motivating data example
4.2.2
The data model
4.2.3
The process model
4.2.4
The parameter model
4.2.5
Simulating data from the prior predictive distribution
4.2.6
Model fitting
4.3
Summary and comments
5
September 1
5.1
Announcements
5.2
Bayesian hierarchical models
5.2.1
Motivating data example
5.2.2
Simulating data from the prior predictive distribution
5.2.3
Model fitting
5.3
Summary and comments
6
September 3
6.1
Announcements
6.2
Bayesian hierarchical models
6.2.1
Model fitting
6.3
The path forward!
7
September 8
7.1
Announcements
7.2
Review
7.3
Extreme precipitation in Kansas
8
September 10
8.1
Announcements
8.2
Extreme precipitation in Kansas
8.3
Intro to GIS
8.3.1
Shapefiles
8.3.2
Raster
8.3.3
Points
8.4
Summary
9
September 15
9.1
Announcements
9.2
Extreme precipitation in Kansas
9.3
Gaussian process
9.3.1
Multivariate normal distribution
10
September 17
10.1
Announcements
10.2
Extreme precipitation in Kansas
10.3
Gibbs sampler
10.3.1
Gibbs sampler: simple example
10.3.2
Gibbs sampler: Bayesian linear model example
11
September 22
11.1
Announcements
11.2
Extreme precipitation in Kansas
11.3
Gibbs sampler
11.3.1
Gibbs sampler: simple example
11.3.2
Gibbs sampler: Bayesian linear model example
11.4
Metropolis algorithm
11.5
Metropolis-Hastings algorithm
12
September 24
12.1
Announcements
12.2
Extreme precipitation in Kansas
12.2.1
Kriging
13
September 28
13.1
Announcements
13.2
Extreme precipitation in Kansas
13.2.1
Generalized additive models
13.2.2
Summary of precipitation example
14
October 1
14.1
Announcements
14.2
Assignment 3
15
October 6
15.1
Announcements
15.2
Assignment 3
16
October 8
16.1
Announcements
16.2
Trajectories
16.3
Descriptive spatio-temporal models for trajectories
17
October 13
17.1
Announcements
17.2
Trajectory data
17.3
Descriptive spatio-temporal models for trajectories
18
October 15
18.1
Announcements
18.2
Descriptive spatio-temporal models for trajectories
19
October 20
19.1
Announcements
19.2
Descriptive spatio-temporal models for trajectories
20
October 22
20.1
Announcements
20.2
Mechanistic spatio-temporal models for trajectories
20.3
Summary
21
October 27
21.1
Announcements
21.2
Spatio-temporal models for disease data
22
October 29
22.1
Announcements
22.2
Spatio-temporal models for disease data
23
November 3
23.1
Announcements
23.2
Spatio-temporal models for disease data
24
November 5
24.1
Announcements
24.2
Spatio-temporal models for disease data
25
November 17
25.1
Announcements
25.2
Earthquake data
25.3
Spatio-temporal models for earthquake data
26
November 19
26.1
Announcements
26.2
Spatio-temporal models for earthquake data
27
Assignment 1
28
Assignment 2
28.1
Motivation and Data
28.2
Goal
28.3
Problems
29
Assignment 3
29.1
Motivation
29.2
Problems
29.3
Part 1
29.4
Part 2
29.5
Part 3
30
Assignment 4
30.1
Motivation
30.2
Data
30.3
Goal
30.4
Problems
31
Assignment 5
31.1
Motivation
31.2
Data
31.3
Goal
31.4
Problems
32
Final project
32.1
Assignment
32.2
Things to consider
32.3
Grading Rubric
33
Teaching assessment 1
Published with bookdown
Applied Spatio-temporal Statistics
24
November 5
24.1
Announcements
Assignment 5 is posted a due Nov. 13
24.2
Spatio-temporal models for disease data
Semi-live example (
R code can be downloaded here
)