PoL workshop on statistical inference
Physical biology is a quantitative science, and biological scientists need to be equipped with tools to analyze quantitative data. This workshop takes a hands-on approach toward development of these tools. Together, we will analyze real data. We will learn how to organize, preserve, and share data sets, create informative interactive graphical displays of data, process images to extract actionable data, and perform basic resampling-based statistical inferences.
Familiarity with the Python programming language and the NumPy package are assumed. Some experience with Pandas is useful, but not required. We will use Bokeh for plotting, and some familiarity with that package is useful as well, but not required. Finally, students should have some experience with calculus and familiarity with basic concepts in probability, though advanced knowledge is not required.
Students should download and unzip the data sets we will use in the workshop.
- Lesson 0: Configuring your computer
- Lesson 1: Pandas and split-apply-combine
- Lesson 2: Exploratory plotting
- Lesson 3: Probability distributions and the plug-in principle
- Lesson 4: Nonparametric inference with hacker stats
- Lesson 5: Generative modeling and parametric inference
- Lesson 6: Maximum likelihood estimation
- Lesson 7: Variate-covariate modeling
- Lesson 8: Model assessment
- Exercise 1. Pandas and split-apply-combine
- Exercise 2. Exploratory plotting
- Exercise 3. Working with probability distributions
- Exercise 4: Nonparametric hacker stats
- Exercise 5: Generative modeling
- Exercise 6: Maximum likelihood estimation
- Exercise 7: MLE with variate-covariate models
- Exercise 8: Model assessment