Statistics
7 posts
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INLA Part 1: Just keep approximating until you get the thing you want
Deriving the central results required to understand INLA, and getting us ready to implement it for some model building in later posts
- bayesian
- inla
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Building Non-Standard Regression Models with JAX
Using JAX's automatic differentiation to train a Zero-Inflated Generalised Poisson regression model
- jax
- regression
- count-data
- ZIP
- poisson
- maximum-likelihood
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Bayesian climbing grades
Bayesian hierarchical model to analyze route setter bias in climbing grades. Uses PyMC to estimate 'true' difficulty accounting for setter variations.
- bayesian
- climbing
- pymc
- statistics
- hierarchical-modeling
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Bivariate Poisson Regression with Expectation-Maximisation
Modeling correlated count data using bivariate Poisson regression with EM algorithm. Handle overdispersion and correlation in count models.
- poisson
- regression
- em-algorithm
- statistics
- python
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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.
- splines
- regression
- bayesian
- pymc
- statistics
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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.
- em-algorithm
- regression
- censored-data
- statistics
- pymc
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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.
- em-algorithm
- gaussian-mixture
- clustering
- statistics
- python