Statistics

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

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

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

Quantile regression for uncertainty estimation beyond the mean. PyTorch implementation for predicting conditional quantiles.

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

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