How to code up a bespoke probability density in Stan

How to write your first Stan program

How to code up a model with discrete parameters in Stan

The intuition behind the Hamiltonian Monte Carlo algorithm

Centered versus non-centered hierarchical models

1. Algorithms and Computation

What are divergent iterations and what to do about them?

The distribution zoo app to help to understand and use probability distributions

Why we typically use dependent sampling to sample from the posterior

Have I converged? Convergence checks in Stan.

Constrained parameters? Use Metropolis-Hastings

11. Introduction to Machine Learning

Effective sample size: representing the cost of dependent sampling

Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)

An introduction to inverse transform sampling

Data Analytics for Beginners | Data Analytics Training | Data Analytics Course | Intellipaat

How to derive a Gibbs sampling routine in general

1. Introduction to Statistics

Introducing Bayes factors and marginal likelihoods

An introduction to importance sampling - optimal importance distributions

Estimating the posterior predictive distribution by sampling