This is the web page for STAT 206 (winter 2021). In what follows DD = David Draper (professor; email address draper@ucsc.edu) and IG  = Isabelle Grenier  (TA; email address igrenier@ucsc.edu).

The catalog description for STAT 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.

  • (4 Jan 2021) 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 and WolframAlpha 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' text in the lower right corner).
  • (5 Jan 2021) When you email IG or me, please put STAT 206 winter 2021 in the subject line; this will enable us to get back to you as quickly as possible.

 

AttachmentSize
PDF icon Lecture notes, part 1 (foundational concepts in the context of an HIV screening case study)550.66 KB
PDF icon Document camera notes (lecture: 5 Jan 2021) (theta, D, script B: HIV case study)421.68 KB
PDF icon Definitions of statistics and data science111.48 KB
PDF icon Lecture notes, part 2 (Bayesian updating in the HIV case study; odds ratios)616.76 KB
Plain text icon R tutorial, part 1 (revised 8 Jan 2021)42.27 KB
PDF icon Quiz 1 in PDF format (REVISED target due date at canvas: 14 Jan 2021)104.66 KB
Plain text icon Quiz 1 in LaTeX format (REVISED target due date at canvas: 14 Jan 2021)4.68 KB
PDF icon Document camera notes (Draper discussion section: 6 Jan 2021) (data types: qual/quant, discrete/continuous, ...)458.96 KB
PDF icon Document camera notes (lecture: 7 Jan 2021; THIS IS THE CORRECT VERSION OF THE CONTENT OF THE 7 JAN LECTURE)990.79 KB
PDF icon Document camera notes (Draper office hour: 7 Jan 2021)676.44 KB
PDF icon Document camera notes (Draper discussion section: 8 Jan 2021) (no document camera content that day: R tutorial)45.81 KB
PDF icon Quiz 2 in PDF format (target due date at canvas: 18 Jan 2021)101.95 KB
Plain text icon Quiz 2 in LaTeX format (target due date at canvas: 18 Jan 2021)6.54 KB
PDF icon Document camera notes (Draper office hour: 11 Jan 2021)471.76 KB
PDF icon Document camera notes (lecture: 12 Jan 2021) (FPR, FDR, Bayesian decision and game theory)805.82 KB
PDF icon Document camera notes (Draper discussion section: 13 Jan 2021) (probability: intuitive interpretations and foundations)1.3 MB
PDF icon Document camera notes (lecture: 14 Jan 2021) (trading off costs and benefits in Case Study 1)404.13 KB
PDF icon Lecture notes, part 3 (Bayesian decision theory; Law of Total Probability)393.43 KB
PDF icon Draper D (1995). Assessment and propagation of model uncertainty (with discussion). JRSS-B, 57, 45-97)6.63 MB
PDF icon In-Home Geriatric Assessment case study (the 7 statistical tasks in data science)241.76 KB
PDF icon Efron B, Hastie T (2016). Computer-Age Statistical Inference. Cambridge: University Press.8.11 MB
Plain text icon Kidney function data (in .txt format) from Efron and Hastie (2016)1.3 KB
PDF icon Document camera notes (Draper office hour: 14 Jan 2021)383.96 KB
PDF icon Document camera notes (Draper discussion section: 15 Jan 2021) (the 7 core statistical data science activities)519.74 KB
PDF icon Lecture notes, part 4 (probability foundations, representative (like-at-random) sampling, IID, SRS)485.05 KB
PDF icon Document camera notes (lecture: 19 Jan 2021) (practical, statistical significance; false-positive errors; stat, ML data science)854.74 KB
PDF icon Document camera notes (Draper office hour: 19 Jan 2021)253.67 KB
PDF icon Lecture notes, part 5 (continuation of part 4)322.8 KB
PDF icon Color palette in R209.41 KB
Plain text icon Kidney function initial data analysis (in .txt format, UPDATED 20 JAN 2021)21.16 KB
PDF icon Document camera notes (Draper discussion section: 20 Jan 2021) (variable transformations; Gaussian mixtures)75.6 KB
PDF icon Take-Home Test 1 in PDF format (please monitor email and Canvas for target deadline updates)223.35 KB
Plain text icon Take-Home Test 1 in LaTeX format (please monitor email and Canvas for target deadline updates)28.9 KB
PDF icon Document camera notes (lecture: 21 Jan 2021) (IMPORTANT: Neyman-style statistical inference)912.41 KB
PDF icon Document camera notes (Draper office hour: 21 Jan 2021)451.39 KB
Plain text icon Simulation of Neyman-style frequentist statistical inference: confidence intervals in the Quiz 2 case study20.65 KB
PDF icon Document camera notes (Draper discussion section: 22 Jan 2021) (Neyman frequentist statistical inference: confidence intervals)568.18 KB
PDF icon Creating and exporting PDF-format plots in R, and importing PDF files (e.g., figures) into your LaTeX documents (PDF version)177.25 KB
Plain text icon Creating and exporting PDF-format plots in R, and importing PDF files (e.g., figures) into your LaTeX documents (LaTeX version)7.39 KB
PDF icon Captopril case study: Neyman-style frequentist inference with a continuous outcome variable)108.22 KB
PDF icon Document camera notes (Draper office hour: 25 Jan 2021)324.85 KB
PDF icon Document camera notes (lecture: 26 Jan 2021) (Neyman-style frequentist inference with a continuous outcome variable)688.62 KB
Plain text icon Captopril case study: data analysis in R, in .txt format4.97 KB
Plain text icon Captopril case study: raw data file in .txt format46 bytes
PDF icon Document camera notes (Draper discussion section: 27 Jan 2021) (IMPORTANT: the bootstrap)336.12 KB
PDF icon Document camera notes (lecture: 28 Jan 2021) (Hypothesis and significance testing; P-values)575.68 KB
PDF icon Document camera notes (Draper office hour: 28 Jan 2021)453.83 KB
Plain text icon R code for the bias investigation in Quiz 310.63 KB
PDF icon Document camera notes (Draper discussion section: 29 Jan 2021) (equivalence of CIs and testing; statsig not = practsig)796.47 KB
PDF icon Quiz 3 in PDF format (REVISED; please use this version; target due date at canvas: 8 Feb 2021)226.5 KB
Plain text icon Quiz 3 in LaTeX format (REVISED; please use this version; target due date at canvas: 8 Feb 2021)23.91 KB
PDF icon Document camera notes (Draper office hour: 1 Feb 2021)57.61 KB
PDF icon Document camera notes (lecture: 2 Feb 2021) (likelihood, log likelihood functions; maximum likelihood estimates)532.88 KB
PDF icon Document camera notes (Draper discussion section: 3 Feb 2021) (estimated SEs for MLEs via log-likelihood-based information)537.12 KB
PDF icon Document camera notes (lecture: 4 Feb 2021) (MLE functional invariance; sufficiency; Bayes's Theorem for Bernoulli theta)652.49 KB
Plain text icon Text and R code from the 2, 3. and 4 Feb 2021 course meetings on likelihood methods41.56 KB
PDF icon Document camera notes (Draper office hour: 4 Feb 2021)475.92 KB
PDF icon Document camera notes (Draper discussion section: 5 Feb 2021) (Conjugate priors; the Beta( alpha, beta ) family of PDFs)637.02 KB
PDF icon Document camera notes (Draper office hour: 8 Feb 2021)408.1 KB
PDF icon Document camera notes (lecture: 9 Feb 2021) (Algorithm for Bayesian conjugate inference)1.61 MB
PDF icon Lecture notes, part 6 (Likelihood and Bayesian analyses of the Bernoulli sampling model; exchangeability)2.47 MB
PDF icon Document camera notes (Draper extra office hour: 9 Feb 2021)191.46 KB
Plain text icon Text and R code from the 10 Feb 2021 morning discussion section on Bayesian analysis in the quiz 2 case study15.85 KB
PDF icon Document camera notes (Draper discussion section: 10 Feb 2021) (LI priors; sequential learning; sampling dist. uncertainty)571.77 KB
PDF icon Document camera notes (Draper extra office hour: 10 Feb 2021)252.41 KB
Plain text icon R code to compute PDF values for members of the Inverse Gamma( alpha, beta ) family of distributions337 bytes
PDF icon Document camera notes (lecture: 11 Feb 2021) (exchangeability; de Finetti's representation theorem)969.56 KB
PDF icon Document camera notes (Draper office hour: 11 Feb 2021)260.26 KB
PDF icon Document camera notes (Draper discussion section: 12 Feb 2021) (pros and cons for Bayes, frequentist analyses; cross-validation)1.41 MB
PDF icon Document camera notes (Draper extra office hour: 12 Feb 2021)487.75 KB
PDF icon Document camera notes (Draper extra office hour: 13 Feb 2021)177.51 KB
PDF icon Document camera notes (Draper extra office hour: 14 Feb 2021)364.95 KB
PDF icon Document camera notes (Draper extra office hour: 15 Feb 2021)39.33 KB
PDF icon Document camera notes (lecture: 16 Feb 2021) (likelihood analysis with k > 1 unknowns)979.69 KB
PDF icon Document camera notes (Draper extra office hour: 16 Feb 2021)346.4 KB
PDF icon Document camera notes (Draper discussion section: 17 Feb 2021) (LoS case study: likelihood, Poisson-Gamma conjugate modeling)820.58 KB
PDF icon Document camera notes (Draper extra office hour: 17 Feb 2021)113.78 KB
PDF icon Document camera notes (lecture: 18 Feb 2021) (Bayesian inference with k >= 2 unknowns: Gaussian sampling model)898.64 KB
PDF icon Lecture notes, part 7 (simulation-based-computation; Monte Carlo and MCMC methods)9.24 MB
PDF icon Document camera notes (Draper office hour: 18 Feb 2021)354.73 KB
PDF icon Document camera notes (Draper discussion section: 19 Feb 2021) (Posterior predictive distributions; predictive model-checking)839.17 KB
Plain text icon R code for likelihood, Bayesian, and bootstrap analyses in the Length of Stay (LoS) case study)19.65 KB
PDF icon Take-Home Test 2 in PDF format (with small typo corrected; please monitor email and Canvas for target deadline updates)371.73 KB
Plain text icon Take-Home Test 2 in LaTeX format (with small typo corrected; please monitor email and Canvas for target deadline updates)65.78 KB
Plain text icon THT 2: R code for making IID draws from the Dirichlet( alpha ) distribution (problem 2(A))279 bytes
Plain text icon THT 2: R code for likelihood and log likelihood visualization in problem 2(B)4.94 KB
Plain text icon THT 2: R code for numerical optimization of the log likelihood function for the likelihood analysis in problem 2(B))4.08 KB
Plain text icon THT 2: R code for empirical Bayes calculations in problem 2(B)5.64 KB
Plain text icon THT 2: rjags and other R code for MCMC calculations in problem 2(B))18.7 KB
Plain text icon THT 2: the random effects rjags model file for MCMC calculations in problem 2(B) (aspirin meta-analysis))261 bytes
Plain text icon THT 2: the fixed effects rjags model file for MCMC calculations in problem 2(B) (aspirin meta-analysis))147 bytes
PDF icon Document camera notes (lecture: 23 Feb 2021) (Numerical optimization of log likelihood functions when k > 1)866.15 KB
Plain text icon R code to illustrate sampling distribution (model) uncertainty in the NB10 case study6.19 KB
Plain text icon R code to explore the degrees of freedom parameter nu in the NB10 t sampling model3.88 KB
Plain text icon R code to optimize and visualize the log likelihood function in the NB10 t model 20.93 KB
PDF icon Document camera notes (Draper extra office hour: 23 Feb 2021)190.08 KB
PDF icon Document camera notes (Draper discussion section: 24 Feb 2021) (multiple comparisons; bootstrap (Bayesian nonparametric method))1.52 MB
PDF icon Document camera notes (Draper extra office hour: 24 Feb 2021)507.46 KB
PDF icon Document camera notes (lecture: 25 Feb 2021) (Monte Carlo methods for approximate Bayesian calculations)722.76 KB
PDF icon Document camera notes (Draper office hour: 25 Feb 2021)459.89 KB
Plain text icon R code for a Monte Carlo re-analysis of the Quiz 2 case study14.21 KB
PDF icon Document camera notes (Draper discussion section: 26 Feb 2021) (the Monte Carlo 4-plot)78.39 KB
Plain text icon R code to lay down the basics of time series analysis for Monte Carlo work24.78 KB
PDF icon Document camera notes (Draper extra office hour: 26 Feb 2021)421.95 KB
PDF icon Document camera notes (Draper extra office hour: 27 Feb 2021)169.62 KB
PDF icon Document camera notes (Draper extra office hour: 28 Feb 2021)421.5 KB
PDF icon Document camera notes (Draper office hour: 1 Mar 2021)391.02 KB
PDF icon Document camera notes (lecture: 2 Mar 2021) (MCMC; the MC and MCMC data sets)641.49 KB
Plain text icon NB10 case study: R code to do MCMC calculations with rjags28 KB
Plain text icon NB10 case study: the rjags t model file for MCMC calculations236 bytes
PDF icon Document camera notes (Draper extra office hour: 2 Mar 2021)610.9 KB
PDF icon Document camera notes (Draper discussion section: 3 Mar 2021) (- 2 * ll.max as GoF criterion; 2-way CV; Exponential Families)1.7 MB
PDF icon Document camera notes (Draper extra office hour: 3 Mar 2021)164.99 KB
PDF icon Document camera notes (lecture: 4 Mar 2021) (Bayesian model comparison; deviance; DIC)679.06 KB
PDF icon Document camera notes (Draper office hour: 4 Mar 2021)48.58 KB
PDF icon Document camera notes (Draper discussion section: 5 Mar 2021) (Bayes factors for model comparison)333.42 KB
PDF icon Document camera notes (Draper extra office hour: 5 Mar 2021)251.4 KB
PDF icon Document camera notes (Draper extra office hour: 6 Mar 2021)248.62 KB
PDF icon Document camera notes (Draper extra office hour: 7 Mar 2021)197.81 KB
PDF icon Document camera notes (Draper office hour: 8 Mar 2021)55.06 KB
PDF icon Document camera notes (lecture: 9 Mar 2021) (Bayes factors; Laplace approximation to log marginal likelihood)2.1 MB
PDF icon Document camera notes (Draper extra office hour: 9 Mar 2021)363.92 KB
PDF icon Document camera notes (Draper extra office hour: 10 Mar 2021)446.82 KB
PDF icon Document camera notes (lecture: 11 Mar 2021) (log scores; comparison of 5 model comparison methods)1.01 MB
PDF icon Document camera notes (Draper office hour: 11 Mar 2021)206.09 KB
PDF icon Document camera notes (Draper discussion section: 9 Mar 2021) (multiple linear regression; logistic regression)1.3 MB
PDF icon THT 3 (final version) in PDF format (absolute deadline: by 11.59pm on Sun 21 Mar 2021)319.98 KB
Plain text icon THT 3 (final version) in LaTeX format (absolute deadline: by 11.59pm on Sun 21 Mar 2021)59.83 KB
Plain text icon THT 3: The mushroom data set (in .txt format) for problem 2(A)373.17 KB
Plain text icon THT 3: Contextual information (in .txt format) about the mushroom data set for problem 2(A)6.86 KB
Plain text icon THT 3: R code supporting your analysis of the mushroom data in problem 2(A)31.72 KB
Plain text icon THT 3: Output of R code for variable selection with BIC in the mushroom data analysis in problem 2(A)12.28 KB
Plain text icon THT 3: The raw data file spam.txt for problem 2(B)728.48 KB
Plain text icon THT 3: Contextual information (in .txt format) about the spam data set for problem 2(B)3.86 KB
Plain text icon THT 3: the main R code file for your analysis in problem 2(B) (with a small bug fix)75.69 KB
Plain text icon THT 3: Analysis helper functions for problem 2(B)4.98 KB
Plain text icon THT 3: output of the summary( ) function call in problem 2(B)7.36 KB
Plain text icon THT 3: Output of the glm( ) function call with the main-effects-only model in problem 2(B)3.84 KB
Plain text icon THT 3: Output of the 'bayesreg' function call in problem 2(B)6.38 KB
PDF icon Document camera notes (Draper extra office hour: 13 Mar 2021)555.03 KB
PDF icon Document camera notes (Draper extra office hour: 14 Mar 2021)86.06 KB
PDF icon Document camera notes (Draper extra office hour: 15 Mar 2021)358.97 KB
PDF icon Document camera notes (Draper extra office hour: 16 Mar 2021)182.01 KB
PDF icon Document camera notes (Draper extra office hour: 17 Mar 2021)48.03 KB
PDF icon Document camera notes (Draper extra office hour: 18 Mar 2021)67.36 KB
PDF icon Document camera notes (extra lecture: 19 Mar 2021) (ML fitting of logistic regression models; Metropolis algorithm)870.13 KB
PDF icon Document camera notes (Draper extra office hour: 19 Mar 2021)42.4 KB
PDF icon Document camera notes (Draper extra office hour: 20 Mar 2021)248.67 KB
PDF icon NIelsen D (2016). Tree Boosting With XGBoost: Why Does XGBoost Win "Every" Machine Learning Competition?2.12 MB
PDF icon Document camera notes (Draper extra office hour: 21 Mar 2021)315.37 KB