Open course material I have written in statistics, programming, and academic writing. Each one is a full, self-contained website with worked examples, rendered mathematics where it is needed, and code.
Bayesian Statistics and Inference
A measure-theoretic, decision-theoretic treatment of Bayesian inference
Derives the major algorithms from first principles, with rigorous proofs of correctness and convergence, posterior analysis (propriety, consistency, identifiability), and both Bayesian and frequentist evaluation perspectives.
- Foundations & conjugate models Exact inference, regression, GLMs
- MCMC theory Metropolis–Hastings, Gibbs, advanced methods
- Approximate inference Variational inference, Gaussian processes
- Theory & criticism Asymptotics, consistency, model checking
A rigorous, mathematically heavy course on hidden Markov models
Built up from measure-theoretic probability through inference, estimation theory, and modern variants — proofs and derivations rather than code snippets, with proof-based problem sets throughout.
- Foundations Markov chains, stochastic matrices, spectral theory
- Inference algorithms Forward–backward, Viterbi, filtering, smoothing
- Estimation Maximum likelihood, EM / Baum–Welch, identifiability
- Advanced theory Asymptotics, continuous states, Kalman filter
- Applications Speech, bioinformatics, finance, epidemiology
R Programming: From Beginner to Expert
A practical R course from core syntax to professional workflows
Covers data analysis, statistical modelling, and reproducible practice in R and RStudio. Written for undergraduate and postgraduate students.
- Beginner to expert R Syntax, data structures, control flow
- Modelling & ML Data manipulation, statistics, machine learning
- Shiny applications Interactive web apps in R
- Developer tools Packages, testing, debugging, code organisation
- Time series case study Analysis and forecasting with Air Passengers
Ever Growing Academic Writing Guide
A handbook on scientific writing, from narrative structure to punctuation
Practical guidance for writing research papers across disciplines, worked through every layer of a manuscript — the story it tells, the sections it is built from, and the individual sentences and words that carry it.
- Writing Story, section, and paragraph level
- Sentences & words Sentence-level control, word choice, vocabulary
- Paper sections Abstract, introduction, method, results, conclusion
- Tools & extras Graphics, LaTeX, rebuttals, collaboration
- Resources Recommended reading and external guides