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1 - Marginal probability for continuous variables
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Classroom Contents
Bayesian Statistics - A Comprehensive Course
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- 1 Bayesian statistics syllabus
- 2 Bayesian vs frequentist statistics
- 3 Bayesian vs frequentist statistics probability - part 1
- 4 Bayesian vs frequentist statistics probability - part 2
- 5 What is a probability distribution?
- 6 What is a marginal probability?
- 7 What is a conditional probability?
- 8 Conditional probability : example breast cancer mammogram part 1
- 9 Conditional probability : example breast cancer mammogram part 2
- 10 Conditional probability - Monty Hall problem
- 11 1 - Marginal probability for continuous variables
- 12 2 Conditional probability continuous rvs
- 13 A derivation of Bayes' rule
- 14 4 - Bayes' rule - an intuitive explanation
- 15 5 - Bayes' rule in statistics
- 16 6 - Bayes' rule in inference - likelihood
- 17 7 Bayes' rule in inference the prior and denominator
- 18 8 - Bayes' rule in inference - example: the posterior distribution
- 19 9 - Bayes' rule in inference - example: forgetting the denominator
- 20 10 - Bayes' rule in inference - example: graphical intuition
- 21 11 The definition of exchangeability
- 22 12 exchangeability and iid
- 23 13 exchangeability what is its significance?
- 24 14 - Bayes' rule denominator: discrete and continuous
- 25 15 Bayes' rule: why likelihood is not a probability
- 26 15a - Maximum likelihood estimator - short introduction
- 27 16 Sequential Bayes: Data order invariance
- 28 17 - Conjugate priors - an introduction
- 29 18 - Bernoulli and Binomial distributions - an introduction
- 30 19 - Beta distribution - an introduction
- 31 20 - Beta conjugate prior to Binomial and Bernoulli likelihoods
- 32 21 - Beta conjugate to Binomial and Bernoulli likelihoods - full proof
- 33 22 - Beta conjugate to Binomial and Bernoulli likelihoods - full proof 2
- 34 23 - Beta conjugate to Binomial and Bernoulli likelihoods - full proof 3
- 35 24 - Bayesian inference in practice - posterior distribution: example Disease prevalence
- 36 25 - Bayesian inference in practice - Disease prevalence
- 37 26 - Prior and posterior predictive distributions - an introduction
- 38 27 - Prior predictive distribution: example Disease - 1
- 39 27 - Prior predictive distribution: example Disease - 2
- 40 29 - Posterior predictive distribution: example Disease
- 41 30 - Normal prior and likelihood - known variance
- 42 31 - Normal prior conjugate to normal likelihood - proof 1
- 43 32 - Normal prior conjugate to normal likelihood - proof 2
- 44 33 - Normal prior conjugate to normal likelihood - intuition
- 45 34 - Normal prior and likelihood - prior predictive distribution
- 46 35 - Normal prior and likelihood - posterior predictive distribution
- 47 36 - Population mean test score - normal prior and likelihood
- 48 37 - The Poisson distribution - an introduction - 1
- 49 38 - The Poisson distribution - an introduction - 2
- 50 39 - The gamma distribution - an introduction
- 51 40 - Poisson model: crime count example introduction
- 52 41 - Proof: Gamma prior is conjugate to Poisson likelihood
- 53 42 - Prior predictive distribution for Gamma prior to Poisson likelihood
- 54 43 - Prior predictive distribution (a negative binomial) for gamma prior to poisson likelihood 2
- 55 44 - Posterior predictive distribution a negative binomial for gamma prior to poisson likelihood
