Code for Discrete and Continuous

Python notebooks for all the computational examples about Discrete and Continuous from Chapter 6 of the book. See here for the full code repository.

• Temperature and precipitation in Mauna Loa. Joint distribution of discrete and continuous variables, marginal distributions, conditional distributions, kernel density estimation
• Height and sex. Mixture model, Gaussian parametric model, joint distribution of discrete and continuous variables, marginal distributions, conditional distributions
• Height and handedness. Joint distribution of discrete and continuous variables, independence, kernel density estimation
• Alzheimer’s diagnostics. Classification, Gaussian random vectors, Gaussian discriminant analysis, quadratic discriminant analysis, linear discriminant analysis, maximum likelihood, parametric models
• Clustering according to height. Gaussian mixture model, expectation maximization algorithm, clustering, unsupervised learning
• Clustering NBA players. Gaussian mixture model, expectation maximization algorithm, clustering, unsupervised learning
• Election poll. Bayesian parametric modeling, beta distribution, prior and posterior distributions, conjugate prior
• How not to predict an election. Bayesian parametric modeling, independence, conditional independence, Monte Carlo method