02:05

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

02:14

Conclusions and references for grammar of graphics

11:37

The path to a good visualisation using grammar of graphics

03:23

Aesthetics and geoms: biological analogy

11:08

Introducing aesthetics and geoms

04:24

Comparing traditional versus grammar of graphics approaches to graphing

04:32

Introduction to grammar of graphics short course

20:28

Centered versus non-centered hierarchical models

09:46

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

21:35

How to code up a model with discrete parameters in Stan

28:48

How to write your first Stan program

14:35

How to code up a bespoke probability density in Stan

21:05

What are divergent iterations and what to do about them?

13:10

Introducing Bayes factors and marginal likelihoods

08:59

Using a Bayes box to calculate the denominator

09:24

Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence

06:21

An introduction to continuous conditional probability distributions

09:10

An introduction to discrete conditional probability distributions.

08:21

Explaining the intuition behind Bayesian inference

12:26

Estimating the posterior predictive distribution by sampling

09:21

The importance of step size for Random Walk Metropolis

06:18

What is the difference between independent and dependent sampling algorithms?

20:40

Explaining the difference between confidence and credible intervals

11:49

An introduction to inverse transform sampling

14:19

An introduction to importance sampling

05:41

The ideal measure of a model's predictive fit

10:08

Explaining the Kullback-Liebler divergence through secret codes

26:04

An introduction to numerical integration through Gaussian quadrature

06:16

An introduction to Jeffreys priors - 3

17:44

Why we typically use dependent sampling to sample from the posterior

15:07

How to derive a Gibbs sampling routine in general

08:06

Why is it difficult to calculate the denominator of Bayes’ rule in practice?

10:30

How to do integration by sampling

05:26

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

08:15

An introduction to continuous probability distributions.

09:41

An example of how an improper prior leads to an improper posterior

04:40

The duality of meaning for likelihoods and probability distributions: the equivalence principle

32:09

The intuition behind the Hamiltonian Monte Carlo algorithm

06:50

An introduction to Jeffreys priors - 1

12:45

An introduction to Jeffreys priors - 2

05:42

Example likelihood model: waiting times between beer orders

13:43

An introduction to importance sampling - optimal importance distributions

18:58

An introduction to Gibbs sampling

10:38

The problem with discrete approximation to integrals or probability densities

09:31

The illusion of uninformative priors

15:39

What is meant by entropy in statistics?

07:36

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

06:16

An introduction to discrete probability distributions

08:35

Maximum likelihood estimation for the beer example model

13:23

The problems with using simple Monte Carlo to determine the marginal likelihood

08:56

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

05:10

Two-dimensional discrete distributions: an introduction

07:47

Random variables and probability distributions.

07:43

On the sensitivity of the marginal likelihood to prior choice

05:30

What is a conjugate prior?

08:37

An introduction to discrete marginal probability distributions

10:03

An introduction to the Beta distribution

17:13

Effective sample size: representing the cost of dependent sampling

04:13

What is meant by overfitting?

10:45

An introduction to the Poisson distribution - 2