MATH 5396 Calendar

MATH 5396 Calendar

Tuesday

Thursday

Additional Info/Questions?

Aug 23
Probability Review

Aug 25
Probability Review
HW#1
.

Aug 30
Probability Review

Sep 1
Probability Review
HW#2
.

Sep 6
Probability Review (the end!)
Background and Single-Parameter Models
Chapter 1 and 2.1

Sep 8
Binomial Model
(2,1-2.4)
HW#3
.

Sep 13
Predictive Distributions
(p. 6-9, 2.3)

Sep 15
Summarizing the Posterior Distribution
(2.3)
HW#4
HW#4: Prob. 1, You should derive E(Y) and Var(Y),
using the formulas for E(theta) and Var(theta) given in (2.7) and (2.8).
This problem is Extra Credit, now.

Sep 20
Normal model, mutliple observations
Other standandard signle-parameter models
(2.6-2.7)

Sep 22
Other standard single parameter models:
Normal, Poisson, Exponential
(2.7)
HW#5
Class handout: nnplots.pdf
R code: for beta distribution plots normalnormal.r

Sep 27
Other standard single parameter models:
Normal, Poisson, Exponential
(2.7, 2.5, 2.8)

Sep 29
Noninformative Prior Distributions
(2.9)
HW#6
Class handout: Poisson Model for Asthma
(in terms of rate and exposure) (Figure 2.6) exposureplots.pdf
R code: for plots exposure.r

Oct 4
Estimating Normalizing Constants (in R)
Construction of Posterior Intevals (Simulation in R and Normal Approx.)
(p. 69 Prob. 11 (a), p. 67 Prob. 1, p.48, p. 26,
p. 70 Prob. 13)

Oct 6
Construction of Posterior Intevals (Simulation in R and Normal Approx.)
(p. 70 Prob. 13)
No Homework!
Midterm Exam next Thurs. Oct. 13!

Oct 11
Review: p. 67-72: Prob. 13 (finish), 10. 22
R: Normalizing in Prob. 1

Oct 13
Midterm Exam (In-class part)
Closed notes and books
Covering: Probability Review, Chapter 1-2, and any other lecture material presented
Take-home Midterm Exam Part II , due Thurs.Oct. 20
.

Oct 18
Multiparameter Models: Normal
(3.1-3.4)

Oct 20
Multiparameter Models: Normal and Multinomial
(3.2-3.5)
See Soltion to Exercise 3.9 for derivation of joint posterior p. 79 eq (3.7)
HW#7
Class handout: R code for generating samples from a scaled inverse-chi-square distriubtion s2.sim.r .

Oct 25
Multiparameter Models: Multinomial
(3.5)

Oct 27
Eample: Bioassay and Logistic Regression
(3.7)
HW#8
Class handout: R code for generating samples from a Dirichlet distriubtion dir.sim.r
Please see introductory R lessons off class web page!
Here is a nice description of logistic regression

Nov. 1
Eample: Bioassay and Logistic Regression
Summary of Elementary Modeling and Computation (3.7, 3.8)

Nov. 3
Bayesian Point Estimation, Credible Intervals, and Hypothesis Testing
(Class Handouts)
HW#9
Here is the R code bioassay.r for HW#9

Nov. 8
Hypothesis Testing (cont.)
(Class Handouts)

Nov. 10
Poster Simulations: Gibbs Sampler
(11.1 - 11.3)
HW#10
Homework 10 Correction to 1 (a) Ho: -1 < theta < 1 vs. H_1: |theta| > 0 SHOULD BE H_1: |theta| > 1 (otherwise the alternative is included in the null!).

Nov. 15
Poster Simulations: Metropolis and Metropolis Hastings
(11.4)

Nov. 17

Poster Simulations: Metropolis and Metropolis Hastings
(11.4)
HW#11
Here is the R code for M and M-H Alg for HW#11 Problem 1
met_hw.r

Nov. 22
No Class, TG Break!

Nov. 24
No Class, TG!
.

Nov. 29
Hierarchical Models
(Chapter 5)

Dec. 1
Hierarchical Models: SAT coaching example
(11.7)
Here is the R code for the SAT coaching example (handout)
sat_gibbs.r and sat_gibbs_plots.r
Guidelines for Data Analysis Project are off class homepage!

Dec. 6
Hierarchical Models: SAT coaching example
Take-home Final Exam , due Tues. Dec. 13
Dataset for Problem 3 birds.dat

Dec. 8
MCMC Convergence Diagnostics
(11.6)
Take-home Final Exam and Project both due
at scheduled Final Exam time!

Dec 13
Final Exam 4-6PM, CU 656
(Chapter 5)
. Have a great break!

Tuesday	Thursday	Additional Info/Questions?
Aug 23 Probability Review	Aug 25 Probability Review HW#1	.
Aug 30 Probability Review	Sep 1 Probability Review HW#2	.
Sep 6 Probability Review (the end!) Background and Single-Parameter Models Chapter 1 and 2.1	Sep 8 Binomial Model (2,1-2.4) HW#3	.
Sep 13 Predictive Distributions (p. 6-9, 2.3)	Sep 15 Summarizing the Posterior Distribution (2.3) HW#4	HW#4: Prob. 1, You should derive E(Y) and Var(Y), using the formulas for E(theta) and Var(theta) given in (2.7) and (2.8). This problem is Extra Credit, now.
Sep 20 Normal model, mutliple observations Other standandard signle-parameter models (2.6-2.7)	Sep 22 Other standard single parameter models: Normal, Poisson, Exponential (2.7) HW#5	Class handout: nnplots.pdf R code: for beta distribution plots normalnormal.r
Sep 27 Other standard single parameter models: Normal, Poisson, Exponential (2.7, 2.5, 2.8)	Sep 29 Noninformative Prior Distributions (2.9) HW#6	Class handout: Poisson Model for Asthma (in terms of rate and exposure) (Figure 2.6) exposureplots.pdf R code: for plots exposure.r
Oct 4 Estimating Normalizing Constants (in R) Construction of Posterior Intevals (Simulation in R and Normal Approx.) (p. 69 Prob. 11 (a), p. 67 Prob. 1, p.48, p. 26, p. 70 Prob. 13)	Oct 6 Construction of Posterior Intevals (Simulation in R and Normal Approx.) (p. 70 Prob. 13) No Homework!	Midterm Exam next Thurs. Oct. 13!
Oct 11 Review: p. 67-72: Prob. 13 (finish), 10. 22 R: Normalizing in Prob. 1	Oct 13 Midterm Exam (In-class part) Closed notes and books Covering: Probability Review, Chapter 1-2, and any other lecture material presented Take-home Midterm Exam Part II , due Thurs.Oct. 20	.
Oct 18 Multiparameter Models: Normal (3.1-3.4)	Oct 20 Multiparameter Models: Normal and Multinomial (3.2-3.5) See Soltion to Exercise 3.9 for derivation of joint posterior p. 79 eq (3.7) HW#7	Class handout: R code for generating samples from a scaled inverse-chi-square distriubtion s2.sim.r	.
Oct 25 Multiparameter Models: Multinomial (3.5)	Oct 27 Eample: Bioassay and Logistic Regression (3.7) HW#8	Class handout: R code for generating samples from a Dirichlet distriubtion dir.sim.r Please see introductory R lessons off class web page! Here is a nice description of logistic regression
Nov. 1 Eample: Bioassay and Logistic Regression Summary of Elementary Modeling and Computation (3.7, 3.8)	Nov. 3 Bayesian Point Estimation, Credible Intervals, and Hypothesis Testing (Class Handouts) HW#9	Here is the R code bioassay.r for HW#9
Nov. 8 Hypothesis Testing (cont.) (Class Handouts)	Nov. 10 Poster Simulations: Gibbs Sampler (11.1 - 11.3) HW#10	Homework 10 Correction to 1 (a) Ho: -1 < theta < 1 vs. H_1: \|theta\| > 0 SHOULD BE H_1: \|theta\| > 1 (otherwise the alternative is included in the null!).
Nov. 15 Poster Simulations: Metropolis and Metropolis Hastings (11.4)	Nov. 17 Poster Simulations: Metropolis and Metropolis Hastings (11.4) HW#11	Here is the R code for M and M-H Alg for HW#11 Problem 1 met_hw.r
Nov. 22 No Class, TG Break!	Nov. 24 No Class, TG!	.
Nov. 29 Hierarchical Models (Chapter 5)	Dec. 1 Hierarchical Models: SAT coaching example (11.7)	Here is the R code for the SAT coaching example (handout) sat_gibbs.r and sat_gibbs_plots.r Guidelines for Data Analysis Project are off class homepage!
Dec. 6 Hierarchical Models: SAT coaching example Take-home Final Exam , due Tues. Dec. 13 Dataset for Problem 3 birds.dat	Dec. 8 MCMC Convergence Diagnostics (11.6)	Take-home Final Exam and Project both due at scheduled Final Exam time!
Dec 13 Final Exam 4-6PM, CU 656 (Chapter 5)	.	Have a great break!