Applications of inverse transform sampling

Polynomial interpolation via Newton’s method

Sampling from mixed exponential distributions using the inverse transform method

Estimating Logistic Regression Coefficents From Scratch in Python

Assessing goodness-of-fit in Python with Scipy

Kernel Density Estimation in Python

Correcting the bias in estimators of population variance

Derivation of the Normal Equations via Least Squares and Maximum Likelihood

Assessing the quality of fit for logistic regression models

Updating sample mean and variance to account for new observations without full recalculation

Derivation of the Poisson Distribution as a Limiting Case of the Binomial PDF

Smoothed Empirical Percentile Matching with Python implementation

Polynomial interpolation via Newton’s divided differences

A matrix factorization approach to linear regression

Creating runoff triangles in R

Using the Freedman-Diaconis Rule to determine optimal histogram bin width

Finding logLikelihood estimates using optim in R

Finding roots of equations in R with uniroot

Generating correlated boostrap reserve estimates with R

Gibbs sampling approaches for Bayesian linear regression

Smoothing data with cubic spline approaches

Using Dijkstra’s Algorithm to Find All Shortest Paths in a Graph

Fitting Logistic regression models with Iterative Reweighted Least Squares in R

An Investigation of the complete and incomplete Beta Functions with use cases