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YouTube

Bayesian statistics: a comprehensive course

Ox educ via YouTube

Syllabus

Bayesian statistics syllabus.
Bayesian vs frequentist statistics.
Bayesian vs frequentist statistics probability - part 1.
Bayesian vs frequentist statistics probability - part 2.
What is a probability distribution?.
What is a marginal probability?.
What is a conditional probability?.
Conditional probability : example breast cancer mammogram part 1.
Conditional probability : example breast cancer mammogram part 2.
Conditional probability - Monty Hall problem.
1 - Marginal probability for continuous variables.
2 Conditional probability continuous rvs.
A derivation of Bayes' rule.
4 - Bayes' rule - an intuitive explanation.
5 - Bayes' rule in statistics.
6 - Bayes' rule in inference - likelihood.
7 Bayes' rule in inference the prior and denominator.
8 - Bayes' rule in inference - example: the posterior distribution.
9 - Bayes' rule in inference - example: forgetting the denominator.
10 - Bayes' rule in inference - example: graphical intuition.
11 The definition of exchangeability.
12 exchangeability and iid.
13 exchangeability what is its significance?.
14 - Bayes' rule denominator: discrete and continuous.
15 Bayes' rule: why likelihood is not a probability.
15a - Maximum likelihood estimator - short introduction.
16 Sequential Bayes: Data order invariance.
17 - Conjugate priors - an introduction.
18 - Bernoulli and Binomial distributions - an introduction.
19 - Beta distribution - an introduction.
20 - Beta conjugate prior to Binomial and Bernoulli likelihoods.
21 - Beta conjugate to Binomial and Bernoulli likelihoods - full proof.
22 - Beta conjugate to Binomial and Bernoulli likelihoods - full proof 2.
23 - Beta conjugate to Binomial and Bernoulli likelihoods - full proof 3.
24 - Bayesian inference in practice - posterior distribution: example Disease prevalence.
25 - Bayesian inference in practice - Disease prevalence.
26 - Prior and posterior predictive distributions - an introduction.
27 - Prior predictive distribution: example Disease - 1.
27 - Prior predictive distribution: example Disease - 2.
29 - Posterior predictive distribution: example Disease.
30 - Normal prior and likelihood - known variance.
31 - Normal prior conjugate to normal likelihood - proof 1.
32 - Normal prior conjugate to normal likelihood - proof 2.
33 - Normal prior conjugate to normal likelihood - intuition.
34 - Normal prior and likelihood - prior predictive distribution.
35 - Normal prior and likelihood - posterior predictive distribution.
36 - Population mean test score - normal prior and likelihood.
37 - The Poisson distribution - an introduction - 1.
38 - The Poisson distribution - an introduction - 2.
39 - The gamma distribution - an introduction.
40 - Poisson model: crime count example introduction.
41 - Proof: Gamma prior is conjugate to Poisson likelihood.
42 - Prior predictive distribution for Gamma prior to Poisson likelihood.
43 - Prior predictive distribution (a negative binomial) for gamma prior to poisson likelihood 2.
44 - Posterior predictive distribution a negative binomial for gamma prior to poisson likelihood.

Taught by

Ox educ

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