An introduction to discrete conditional probability distributions.

Online conference at Oxford University: Inference for expensive systems in mathematical biology

Introduction to grammar of graphics short course

Comparing traditional versus grammar of graphics approaches to graphing

The path to a good visualisation using grammar of graphics

Aesthetics and geoms: biological analogy

Conclusions and references for grammar of graphics

Introducing aesthetics and geoms

Centered versus non-centered hierarchical models

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

How to write your first Stan program

How to code up a model with discrete parameters in Stan

What are divergent iterations and what to do about them?

How to code up a bespoke probability density in Stan

Introducing Bayes factors and marginal likelihoods

An introduction to importance sampling - optimal importance distributions

An introduction to Jeffreys priors - 2

The problem with discrete approximation to integrals or probability densities

An introduction to discrete conditional probability distributions.

An introduction to continuous conditional probability distributions

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

Maximum likelihood estimation for the beer example model

Example likelihood model: waiting times between beer orders

Using the Random Walk Metropolis algorithm to sample from a cow surface distribution

An introduction to continuous probability distributions.