New York University

- Sampling from a grid-approximate posterior
- Sampling to summarize
- Sampling to simulate prediction

Sampling the Imaginary

McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd ed.). Chapman and Hall/CRC.

Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd edition)

Goal: to summarize and interpret from the posterior distribution

Example questions to answer:
- How much posterior probability lies below some parameter value?
- How much posterior probability lies between two parameter values?
- Which parameter value marks the lower 5% of the posterior probability?
- Which range of parameter values contains 90% of the posterior probability?
- Which parameter value has highest posterior probability?

We can answer these question by the below 3 categories:
1. Intervals of defined boundaries
2. Intervals of defined mass
3. Point estimates

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