This is the web page for AMS 206 (winter 2019). In what follows DD = David Draper (lecturer; email address draper@ucsc.edu), RG  = Rene Gutierrez  (TA; email address rgutie17@ucsc.edu), and BE = Baskin Engineering.

The catalog description for AMS 206 is as follows:

Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, ...), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models). Prerequisite(s): course 131 or 203, or by permission of the instructor. Enrollment is restricted to graduate students except by instructor permission.

  • (27 Dec 2018) Announcements will be posted in this section. The first Attachment section below will contain (scanned) PDF copies of the lecture notesdocument camera notes and extra notes, as well as case studies and R Maple  and WinBUGS code; the second Attachment section will contain secure documents and is invisible until you log into the web page with your CruzID Blue password (click on the tiny 'Log In' in the lower right corner).
  • (2 Jan 2019) The webcasts for the course have now been scheduled. To watch a video, go to https://webcast.ucsc.edu/ and click on the Video List link under AMS 206; you'll then be taken to a login page. Type ams-206-1 in the top yellow box and bayesian-data-science in the yellow box below that; if you check the Remember me box, you won't have to type these things in during subsequent logins on the machine where you just entered those details. When you now click the blue Login box, you'll go to the Course Webcasts page, and you can now watch any videos you like. The nice thing about the webcasts is, unlike me when I'm lecturing, you can pause, rewind and fast forward the videos.
  • (14 Jan 2019, revised 28 Jan 2019, revised 5 Feb 2019) Here are the office hours for AMS 206 this quarter, all of which will take place in Baskin 358 until further notice: Mon 4-5.30pm (DD); Mon 6-7pm (RG); Tue 3.30-5pm (DD); Wed 12.30-2pm (DD); Wed 6-7pm (RG); Thu 3.30-5pm (DD); Fri noon-1pm (RG); Fri 4-5.30pm (DD).

 

 

AttachmentSize
PDF icon Document camera notes (8 Jan 2019)50.26 KB
PDF icon Lecture notes, part 1 [pages 1- 12] (8 Jan 2019)550.66 KB
PDF icon Document camera notes (10 Jan 2019)51.51 KB
PDF icon Lecture notes, part 2 [pages 12-28] (10 Jan 2019)616.76 KB
PDF icon Quiz 1 in .pdf format (target date at canvas 20 Jan 2019, drop-dead date 29 Jan 2019 [NEW DEADLINE])104.78 KB
Plain text icon Quiz 1 in .tex format (target date at canvas 20 Jan 2019, drop-dead date 29 Jan 2019 [NEW DEADLINE])4.67 KB
Plain text icon R tutorial, part 138.33 KB
Plain text icon R and Maple code for Case Study 110.61 KB
PDF icon Document camera notes (15 Jan 2019)81.01 KB
PDF icon Lecture notes, part 3 [pages 29-39] (15 Jan 2019)393.43 KB
PDF icon Document camera notes (16 Jan 2019)12.07 KB
PDF icon Quiz 2 in .pdf format (target date at canvas 25 Jan 2019, drop-dead date 15 Mar 2019)100.22 KB
Plain text icon Quiz 2 in .txt format (target date at canvas 25 Jan 2019, drop-dead date 15 Mar 2019)5.92 KB
PDF icon Document camera notes (17 Jan 2019)20.84 KB
PDF icon Lecture notes, part 4 [pages 39-51] (17 Jan 2019)485.05 KB
PDF icon Take-Home Test 1 in .pdf format with typos fixed (target date at canvas 5 Feb 2019, drop-dead date 15 Mar 2019)208.1 KB
Plain text icon Take-Home Test 1 in .tex format with typos fixed (target date at canvas 5 Feb 2019, drop-dead date 15 Mar 2019)26.93 KB
PDF icon Document camera notes (22 Jan 2019, revised)122.64 KB
PDF icon Lecture notes, part 5 [pages 51-59] (22 Jan 2019)322.8 KB
Plain text icon R code for the Central Limit Theorem simulation in Case Study 213.36 KB
PDF icon Document camera notes (24 Jan 2019, revised)135.41 KB
PDF icon Document camera notes (29 Jan 2019)168.64 KB
PDF icon Lecture notes, part 6 [pages 60-62] (29 Jan 2019)102.29 KB
PDF icon Document camera notes (31 Jan 2019)161.51 KB
PDF icon Lecture notes on maximum likelihood, exchangeability, and conjugate modeling2.47 MB
PDF icon Document camera notes (5 Feb 2019)153.24 KB
PDF icon Quiz 3 in .pdf format (drop-dead date at canvas 13 Feb 2019)159.27 KB
Plain text icon Quiz 3 in .tex format (drop-dead date at canvas 13 Feb 2019)8.28 KB
Plain text icon Data set ('actual-minus-predicted.txt') for Quiz 32.02 KB
PDF icon Document camera notes (7 Feb 2019)153.76 KB
Plain text icon R code for drawing random samples from a Dirichlet distribution (for Take-Home Test 2 problem 2(A))278 bytes
PDF icon Document camera notes (12 Feb 2019)61.77 KB
PDF icon Document camera notes (14 Feb 2019)89.34 KB
PDF icon Document camera notes (19 Feb 2019)155.96 KB
PDF icon Document camera notes (21 Feb 2019)83.18 KB
PDF icon Lecture notes on simulation-based computation9.24 MB
PDF icon Document camera notes (26 Feb 2019)157.94 KB
Plain text icon rjags analysis of the NB10 data set15.33 KB
Plain text icon Model file for NB10 rjags analysis236 bytes
Plain text icon R code to make the likelihood and log-likelihood plots in problem 2(B) of Take-Home Test 23.7 KB
Plain text icon R code to maximize the log-likelihood function via 'optim' in problem 2(B) on Take-Home Test 23.31 KB
Plain text icon R code to make empirical Bayes calculations in Take-Home Test 2 problem 2(B)4.61 KB
Plain text icon R code to make MCMC calculations using rjags in Take-Home Test 2 problem 2(B)10.92 KB
Plain text icon Model file for rjags analysis in Take-Home Test 2 problem 2(B)261 bytes
PDF icon Document camera notes (28 Feb 2019)80.89 KB
Plain text icon R code for OPTIONAL *interactive* contour and perspective plots in Take-Home Test 2 problem 2(B)6.06 KB
PDF icon FINAL version (with typos corrected) of Take-Home Test 2 in PDF format (due at canvas by 11.59pm on Sun 10 Mar 2019)280.48 KB
Plain text icon FINAL version (with typos corrected) of Take-Home Test 2 in LaTeX format (due at canvas by 11.59pm on Sun 10 Mar 2019)56.31 KB
PDF icon Lecture notes on Bayesian hierarchical modeling3.34 MB
Plain text icon In-Home Geriatric Assessment (IHGA) case study: random-effects poisson regression (REPR) model file416 bytes
Plain text icon In-Home Geriatric Assessment (IHGA) case study: random-effects poisson regression (REPR) data file4.15 KB
Plain text icon In-Home Geriatric Assessment (IHGA) case study: random-effects poisson regression (REPR) initial values file56 bytes
PDF icon Document camera notes (5 Mar 2019)146 KB
PDF icon Browne W, Draper D (2004). A comparison of Bayesian and likelihood-based methods for fitting multilevel models.496.78 KB
PDF icon Document camera notes (7 Mar 2019)91.78 KB
PDF icon Sudipto Banerjee's notes on the conjugate Bayesian analysis of the multiple linear regression model154.4 KB
PDF icon Document camera notes (12 Mar 2019)127.36 KB
Plain text icon R code for linear model analyses of the kidney function data36.25 KB
Plain text icon The kidney function data set1.46 KB
Plain text icon Optional lecture on Bayesian logistic regression: diabetes data set in .txt format23.55 KB
Plain text icon Optional lecture on Bayesian logistic regression: R code for analysis of diabetes data set6.38 KB
PDF icon Optional lecture on Bayesian logistic regression: Gramacy and Polson (2012) (PDF)457.01 KB
PDF icon Optional lecture on Bayesian logistic regression: PDF documentation for CRAN package reglogit98.53 KB
PDF icon Lecture notes on maximum-likelihood and Bayesian fitting of logistic regression models142.83 KB
PDF icon Document camera notes (14 Mar 2019)128.26 KB
PDF icon Take-Home Test 3 (FINAL version) in PDF format; due at canvas by 11.59pm Sun 24 Mar 2019261.91 KB
Plain text icon Take-Home Test 3 (FINAL version) in LaTeX format; due at canvas by 11.59pm Sun 24 Mar 201947.84 KB
Plain text icon Data set 'mushroom-data.txt' for problem 2(A) in Take-Home Test 3373.17 KB
Plain text icon Text file containing contextual information about the mushroom data set for problem 2(A) in Take-Home Test 36.86 KB
Plain text icon R code to perform much of the analysis in Take-Home Test 3 problem 2(A) 28.99 KB
Plain text icon Output of the BIC-based model search in the analysis of the mushroom data for problem 2(A) in Take-Home Test 312.28 KB
Plain text icon Contextual information in the spam case study (problem 2(B), Take-Home-Test 3)3.87 KB
Plain text icon spam data set in .txt format, for problem 2(B), Take-Home-Test 3728.48 KB
Plain text icon R code: FINAL version of partial analysis of the spam data for problem 2(B) in Take-Home Test 327.58 KB
Plain text icon R code: FINAL version of data analysis functions for problem 2(B), Take-Home-Test 33.66 KB
Plain text icon Output of R code: variable summary produced in code block 1 (problem 2(B), Take-Home-Test 3)7.36 KB
PDF icon Document camera notes (optional lecture, 19 Mar 2019)97.47 KB
Plain text icon R code for parallel MCMC in the NB10 case study (requires model file: next download below this one)6.87 KB
Plain text icon NB10 model file for parallel MCMC calculations233 bytes
PDF icon Lecture notes on Bayes factors, Laplace approximations, and BIC1.06 MB
PDF icon Lecture notes on Metropolis and Gibbs sampling1.38 MB
PDF icon Document camera notes (optional lecture, 22 Mar 2019)165.19 KB
Plain text icon De-identified grade list for AMS 206, winter 20194.62 KB