Ben Lambert
This channel is intended to provide a detailed explanation of the majority of undergraduate & graduate courses in econometrics, with as much emphasis as possible on intuition & examples rather than hardcore mathematics. The undergraduate course in particular which I provide does not use any linear algebra in the given derivations. The graduate course extends the undergraduate course by covering the same topics more completely using matrix algebra, and going into the asymptotic behaviour of estimators in more depth.
Recently, I have published a book on Bayesian inference and include here a video series on this topic.
Online conference at Oxford University: Inference for expensive systems in mathematical biology
Conclusions and references for grammar of graphics
The path to a good visualisation using grammar of graphics
Aesthetics and geoms: biological analogy
Introducing aesthetics and geoms
Comparing traditional versus grammar of graphics approaches to graphing
Introduction to grammar of graphics short course
Centered versus non-centered hierarchical models
The distribution zoo app to help to understand and use probability distributions
How to code up a model with discrete parameters in Stan
How to write your first Stan program
How to code up a bespoke probability density in Stan
What are divergent iterations and what to do about them?
Introducing Bayes factors and marginal likelihoods
Using a Bayes box to calculate the denominator
Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence
An introduction to continuous conditional probability distributions
An introduction to discrete conditional probability distributions.
Explaining the intuition behind Bayesian inference
Estimating the posterior predictive distribution by sampling
The importance of step size for Random Walk Metropolis
What is the difference between independent and dependent sampling algorithms?
Explaining the difference between confidence and credible intervals
An introduction to inverse transform sampling
An introduction to importance sampling
The ideal measure of a model's predictive fit
Explaining the Kullback-Liebler divergence through secret codes
An introduction to numerical integration through Gaussian quadrature
An introduction to Jeffreys priors - 3
Why we typically use dependent sampling to sample from the posterior