Ben Lambert

Using bees to demonstrate the importance of overdispersed Markov chains in MCMC

Interpreting regression coefficients in log models part 1

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

An introduction to Jeffreys priors - 1

Random walk with drift

The matrix formulation of econometrics - example

Comparing traditional versus grammar of graphics approaches to graphing

How to code up a bespoke probability density in Stan

The problem with discrete approximation to integrals or probability densities

Conclusions and references for grammar of graphics

An introduction to discrete probability distributions

Autoregressive order 1 process - conditions for stationary in variance

An introduction to continuous conditional probability distributions

Maximum likelihood estimation for the beer example model

Introducing Bayes factors and marginal likelihoods

Independent two sample t test for populations with equal variances

An introduction to Jeffreys priors - 2

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

Introduction to grammar of graphics short course

Aesthetics and geoms: biological analogy

Example likelihood model: waiting times between beer orders

Likelihood ratio test - introduction

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

How to write your first Stan program

Centered versus non-centered hierarchical models

The intuition behind the Hamiltonian Monte Carlo algorithm

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

An introduction to Gibbs sampling

How to code up a model with discrete parameters in Stan

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

The Law of Iterated Expectations: an introduction

Random Effects vs Fixed Effects estimators

Simultaneous equation models - an introduction

The path to a good visualisation using grammar of graphics

F test - the similarity with the t test

Central Limit Theorem - proof part 2

The Rubin Causal model - an introduction

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

Model implied variance-covariance matrix of indicators (matrix form) - part 1

Propensity score matching: an introduction

Using a Bayes box to calculate the denominator

Linearity in parameters - Gauss-Markov

Instrumental Variables - an introduction

Two Stage Least Squares - example

Continuous variables - interaction term interpretation

Serial correlation biased standard errors (advanced topic) - part 1

Undergraduate econometrics syllabus

The Characteristic Function of a Normal Random Variable - part 1 (advanced)

Serial correlation biased standard errors (advanced topic) - part 2

An introduction to continuous marginal probability distributions

How to do integration by sampling