Ben Lambert
@SpartacanUsuals - 497 本の動画
チャンネル登録者数 14万人
This channel is intended to provide a detailed explanation of the majority of undergraduate & graduate courses in econometrics, with as much emphasis as poss...
最近の動画
2:05
Online conference at Oxford University: Inference for expensive systems in mathematical biology
2:14
Conclusions and references for grammar of graphics
11:37
The path to a good visualisation using grammar of graphics
3:23
Aesthetics and geoms: biological analogy
11:08
Introducing aesthetics and geoms
4:24
Comparing traditional versus grammar of graphics approaches to graphing
4:32
Introduction to grammar of graphics short course
20:28
Centered versus non-centered hierarchical models
9:46
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
9:24
Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence
8:59
Using a Bayes box to calculate the denominator
9:10
An introduction to discrete conditional probability distributions.
6:21
An introduction to continuous conditional probability distributions
9:21
The importance of step size for Random Walk Metropolis
12:26
Estimating the posterior predictive distribution by sampling
8:21
Explaining the intuition behind Bayesian inference
5:26
Using the Random Walk Metropolis algorithm to sample from a cow surface distribution
10:30
How to do integration by sampling
8:06
Why is it difficult to calculate the denominator of Bayes’ rule in practice?
15:07
How to derive a Gibbs sampling routine in general
17:44
Why we typically use dependent sampling to sample from the posterior
6:16
An introduction to Jeffreys priors - 3
26:04
An introduction to numerical integration through Gaussian quadrature
10:08
Explaining the Kullback-Liebler divergence through secret codes
5:41
The ideal measure of a model's predictive fit
14:19
An introduction to importance sampling
11:49
An introduction to inverse transform sampling
20:40
Explaining the difference between confidence and credible intervals
6:18
What is the difference between independent and dependent sampling algorithms?
8:56
The difficulty with real life Bayesian inference: high multidimensional integrals (and sums)
13:23
The problems with using simple Monte Carlo to determine the marginal likelihood
8:35
Maximum likelihood estimation for the beer example model
6:16
An introduction to discrete probability distributions
7:36
What is meant by independent sampling and how can it be used to understand a distribution?
15:39
What is meant by entropy in statistics?
9:31
The illusion of uninformative priors
10:38
The problem with discrete approximation to integrals or probability densities
18:58
An introduction to Gibbs sampling
13:43
An introduction to importance sampling - optimal importance distributions
5:42
Example likelihood model: waiting times between beer orders
12:45
An introduction to Jeffreys priors - 2
6:50
An introduction to Jeffreys priors - 1
32:09
The intuition behind the Hamiltonian Monte Carlo algorithm
4:40
The duality of meaning for likelihoods and probability distributions: the equivalence principle
9:41
An example of how an improper prior leads to an improper posterior
8:15
An introduction to continuous probability distributions.
7:21
Two-dimensional continuous distributions: an introduction
8:27
An introduction to the Bernoulli and binomial distributions
5:00
What does it mean to sample from a distribution?
10:45
An introduction to the Poisson distribution - 2
4:13
What is meant by overfitting?
17:13
Effective sample size: representing the cost of dependent sampling
10:03
An introduction to the Beta distribution
8:37
An introduction to discrete marginal probability distributions
5:30
What is a conjugate prior?
7:43
On the sensitivity of the marginal likelihood to prior choice
7:47
Random variables and probability distributions.
5:10
Two-dimensional discrete distributions: an introduction
6:02
An introduction to the concept of a sufficient statistic
7:03
Breast cancer example use of Bayes' rule - 2
6:01
Using bees to demonstrate the importance of overdispersed Markov chains in MCMC
10:30
An introduction to continuous marginal probability distributions
9:57
An introduction to the Poisson distribution - 1
9:12
How to use rejection sampling to uniformly sample within a cow's boundaries
7:47
Why is a likelihood not a probability distribution?
6:56
What is meant by overfitting?
11:28
An introduction to the Random Walk Metropolis algorithm
8:33
An introduction to mutual information
5:59
Breast cancer example use of Bayes' rule - 1
10:37
An introduction to rejection sampling
10:25
An introduction to reference priors
7:09
An introduction to central limit theorems
13:14
Constrained parameters? Use Metropolis-Hastings
11:16
What is a posterior predictive check and why is it useful?
17:28
An introduction to the gamma distribution
11:20
Evaluating model fit through AIC, DIC, WAIC and LOO-CV
15:27
The syllabus covered by the book and YouTube course
2:04
Cauchy Schwarz Inequality Proof part 2
5:30
Finite sample properties of Wald + Score and Likelihood Ratio test statistics
6:25
Cauchy Schwarz Inequality Proof new
8:58
Econometric model building - general to specific
7:23
Bayesian posterior sampling
8:55
Bayesian statistics syllabus
6:18
Serial correlation - The Durbin-Watson test
7:08
Moving Average processes - Stationary and Weakly Dependent
4:48
Factor Analysis - model representation - part 2
7:42
Factor Analysis - an introduction
4:51
Model implied variance-covariance matrix of indicators (matrix form) - part 1
5:13
Variance-covariance matrix using matrix notation of factor analysis
4:33
Factor Analysis - model representation
5:29
Covariance between indicators and factors
3:22
Factor Analysis - model representation - part 4 (matrix form)
4:12
Factor analysis assumptions
4:32
Model implied variance-covariance matrix - an example
9:56
Factor Analysis - model representation - part 3 (matrix form)
