Statistics

Building Non-Standard Regression Models with JAX

Using JAX’s automatic differentiation to train a Zero-Inflated Generalised Poisson regression model

Bayesian climbing grades

Bayesian hierarchical model to analyze route setter bias in climbing grades. Uses PyMC to estimate ’true’ difficulty accounting for setter variations.

Bivariate Poisson Regression with Expectation-Maximisation

Modeling correlated count data using bivariate Poisson regression with EM algorithm. Handle overdispersion and correlation in count models.

Cubic Spline Expansions with Logistic Regression

Flexible non-linear regression using cubic spline basis functions. Bayesian approach with PyMC for smooth curve fitting and uncertainty quantification.

The EM Algorithm Part 2: Censored Linear Regression

Part 2: Applying EM algorithm to censored linear regression. Handle missing data and truncated observations in statistical modeling.

The EM Algorithm Part 1: Gaussian Mixture Models

Part 1: Introduction to EM algorithm with Gaussian Mixture Models. Learn expectation-maximization for unsupervised clustering and density estimation.