Variate-covariate modeling exercises

About the course
1 What are we doing?
Polars and split-apply-combine
Data display
Probability: The foundation for generative modeling
Sampling out of probability distributions
- 12 Random number generation
- 13 Random number generation using Numpy
Simulating generative distributions
Markov chain Monte Carlo
Bayesian modeling and inference
Statistical inference with Markov chain Monte Carlo
Mixture models
- 29 Mixture models and label switching with MCMC
Principled inference pipelines
Model assessment
- 33 Model comparison
- 34 Model comparison in practice
Summarizing posterior distributions with maxima
Variate-covariate models
- 42 Model building
- 43 Variate-covariate models with MCMC
Hierarchical models
Principal component analysis and related models
Hidden Markov models
- 52 Hidden Markov models
Generalized linear models
- 53 Generalized linear models: An introduction
- 54 GLMs applied to neurons and aggression
Polars and split-apply-combine exercises
Data visualization exercises
Probability exercises
Sampling and simulation exercises
Introductory MCMC exercises
- 71 Sampling out of a bivariate Normal distribution
- 72 Funnel of hell
Bayesian modeling exercises
Inference with MCMC exercises
Variate-covariate modeling exercises
- 81 Determining a dissociation constants
- 82 Diagnosing nonidentifiability with MCMC
Appendices
- Notation
  - A Notation
- Computing

Variate-covariate modeling exercises

Variate-covariate models are “just” another kind of Bayesian model. So, these exercises are really just more of the same!

80 Inferring a changepoint

81 Determining a dissociation constants

WIS-Stats 2025