How to code up a model with discrete parameters in Stan

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

How to code up a bespoke probability density in Stan

What are divergent iterations and what to do about them?

Why we typically use dependent sampling to sample from the posterior

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

Statistical Methods Series: NIMBLE

How to write your first Stan program

An introduction to importance sampling - optimal importance distributions

Effective sample size: representing the cost of dependent sampling

Introducing Bayes factors and marginal likelihoods

Centered versus non-centered hierarchical models

How to derive a Gibbs sampling routine in general

The difficulty with real life Bayesian inference: high multidimensional integrals (and sums)

What is meant by independent sampling and how can it be used to understand a distribution?

Estimating the posterior predictive distribution by sampling

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An introduction to importance sampling

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

What is a posterior predictive check and why is it useful?

MIT 6.S191: Reinforcement Learning