Applied Bayesian Modeling and Prediction
Preface
1
Day 1 (January 21)
1.1
Welcome and preliminaries
1.2
Intro to Bayesian statistical modelling
1.3
Example with linear models
1.4
Estimation and inference
1.5
Loss function approach
1.6
Likelihood-based approach
1.7
Bayesian approach
1.8
Low information content data
2
Day 2 (January 25)
2.1
Announcements
3
Day 3 (January 28)
3.1
Announcements
4
Day 4 (January 30)
4.1
Announcements
4.2
Example with differential equations
4.3
Building our first statistical model
4.4
Numerical Integration
5
Day 5 (February 4)
5.1
Announcements
5.2
Building our first statistical model
5.3
Numerical Integration
5.4
Monte Carlo Integration
6
Day 6 (February 6)
6.1
Announcements
6.2
Building our first statistical model
6.3
Monte Carlo Integration
7
Day 7 (February 11)
7.1
Announcements
7.2
Building our first statistical model
7.3
Rejection sampling
7.4
Introduction to Metropolis-Hastings algorithm
8
Day 8 (February 13)
8.1
Announcements
8.2
Introduction to Metropolis-Hastings algorithm
8.3
Our second statistical model
9
Day 9 (February 18)
9.1
Announcements
9.2
Introduction to Metropolis-Hastings algorithm
9.3
Our second statistical model
10
Day 10 (February 20)
10.1
Announcements
10.2
Our second statistical model
11
Day 11 (February 25)
11.1
Announcements
11.2
Our second statistical model
11.3
Summary and future direction
11.4
The Bayesian Linear Model
12
Day 12 (February 27)
12.1
Announcements
13
Day 13 (March 4)
13.1
Announcements
13.2
The Bayesian Linear Model
14
Day 14 (March 6)
14.1
Announcements
14.2
The Bayesian Linear Model
14.3
Bayesian prediction
15
Day 15 (March 11)
15.1
Announcements
15.2
Bayesian prediction
15.3
Time series
16
Day 16 (March 13)
16.1
Announcements
16.2
Time series
17
Day 17 (March 25)
17.1
Announcements
18
Day 18 (March 27)
18.1
Announcements
18.2
Time series
19
Day 19 (April 1)
19.1
Announcements
19.2
Time series
19.3
Data fusion
20
Day 20 (April 3)
20.1
Announcements
20.2
Data fusion
21
Day 21 (April 8)
21.1
Announcements
22
Day 22 (April 10)
22.1
Announcements
22.2
Gaussian process
22.2.1
Multivariate normal distribution
22.3
Extreme precipitation in Kansas
23
Day 23 (April 15)
23.1
Announcements
24
Day 24 (April 17)
24.1
Announcements
24.2
Extreme precipitation in Kansas
25
Day 25 (April 22)
25.1
Announcements
26
Day 26 (April 24)
26.1
Announcements
26.2
Model selection/comparison
27
Day 27 (April 29)
27.1
Announcements
27.2
Model selection/comparison
27.3
Spatio-temporal models
28
Day 28 (May 1)
28.1
Announcements
28.2
Spatio-temporal models
29
Activity 1
30
Activity 2
31
Activity 3
32
Assignment 3 (Guide)
33
Activity 4
34
Activity 4 (Guide)
35
Activity 5
36
Final project
36.1
Grading Rubric
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Applied Bayesian Modeling and Prediction
25
Day 25 (April 22)
25.1
Announcements
In class workday