title: "Schedule :: Advanced Biological Statistics II: Bio 610, Winter 2018" author: "Peter Ralph" date: "9 January 2018" ...
The tentative schedule (subject to adjustment, especially towards the end) is (K referes to Kruschke):
Week 1 (1/9)
: (slides) Recap of probability and likelihood;
central limit theorem (
- **Homework:** (due 1/16) [MLE for beta-binomial](hws/week_1.html) :: [solution](hws/week_1_soln.html)
- **Demo:** (from 1/11) [Beta-binomial analysis](demos/beta_binom.html)
Week 2 (1/16)
: (slides) Introduction to MCMC and Stan for sampling from posterior distributions, hierarchical models for binary responses, shrinkage. (K ch. 7, 9 and Intro to Stan)
- **Homework:** (due 1/26) [Error rates in ancient DNA](hws/week_2.html) :: [solution](hws/week_2_soln.html)
Week 3 (1/23)
: (slides) Assessing power, model choice, and using simulation: looking more at shrinkage, posterior predictive sampling, model comparison. Logistic regression: robustly, including categorical factors. (K ch 13 and 21, with a bit of chapters 10-12)
Week 4 (1/30)
: (slides) Assessing power, model choice, and using simulation: looking more at shrinkage, : Count data: using Poisson regression and hierarchical modeling to fit overdispersion. Model selection by crossvalidation. (K ch 24)
- **Homework:** (due 2/9) [Coverage](hws/week_4.html)
Week 5 (2/6)
: (slides) Continuous ("metric") data: groupwise means, univariate regression, robust regression by adjusting the noise distribution, friends of ANOVA. (K ch 16, 17, 18)
- **Homework:** (due 2/19) [Pipefish](hws/week_6.html)
Week 6 (2/13)
: (slides) Sparsifying priors and variable selection. An in-depth applied example, cumulative. (K ch 19, 20)
Week 7 (2/20)
: (slides) Optimization and variational Bayes in Stan. Review of model building.
- **Homework:** (due 2/27) [Diabetes](hws/week_7.html)
Week 8 (2/27)
: (slides) Clustering and categorization: nonnegative matrix factorization. Also: t-SNE in Stan.
Week 9 (3/6)
: (slides) Time series: modeling local dependency, smoothing. Conditional independence.
Week 10 (3/13)
: (slides) Spatial and network covariance: sharing power between related locations. Priors to constrain visualization (e.g., regularized PCA).
And, finally: a review.