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Fundamentals of Bayesian Data Analysis in R

Rasmus Bååth

Data Scientist

We have covered

Fundamentals of Bayesian Data Analysis in R

We have covered

Fundamentals of Bayesian Data Analysis in R

We have covered

Fundamentals of Bayesian Data Analysis in R

We have covered

Fundamentals of Bayesian Data Analysis in R

We have covered

  • Computational methods
    • Rejection sampling
    • Grid approximation
    • Markov chain Monte Carlo (MCMC)
Fundamentals of Bayesian Data Analysis in R

We have covered

  • Generative models:

Fundamentals of Bayesian Data Analysis in R
  • Working with samples representing probability distributions:
> head(sample)

mu    sigma
39.39 10.18
39.39 21.77
40.90 20.26
45.45 13.20
34.84 12.70
40.90 12.70

pred_iq <- rnorm(10000, mean = sample$mu, sd = sample$sigma)
sum(pred_iq >= 60) / length(pred_iq)

0.0901
Fundamentals of Bayesian Data Analysis in R

Things we didn't cover

  • That a Bayesian approach can be used for much more than simple models.
  • How to decide what priors and models to use.
  • How Bayesian statistics relate to classical statistics.
  • More advanced computational methods.
  • More advanced computational tools.
Fundamentals of Bayesian Data Analysis in R

Things we didn't cover

Fundamentals of Bayesian Data Analysis in R

Go explore Bayes!

Fundamentals of Bayesian Data Analysis in R

Bye and thanks!

Fundamentals of Bayesian Data Analysis in R

Let's practice!

Fundamentals of Bayesian Data Analysis in R

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