This textbook will primarily be used for subsequent nodes about Bayesian Statistics. It provides a general overview of what Bayesian Statistics is, including example problems and codes. 

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

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

University of Michigan - Ann Arbor

Bayesian statistics relies on probability distributions to process information in a logical manner. Compared to other statistical frameworks, Bayesian statistics yields more intuitive interpretations, and has hence been used more widely by statisticians and scientists.

Bayesian Statistics

- Statistical Golems
- Plausibility
- Posterior Distribution
- Sampling the Imaginary
- Building a Bayesian Model
- Gaussian Distribution
- Correlation vs. Causation
- Linear Models
- Multiple Regression Models
- Perils of Multiple Regression
- Underfitting vs Overfitting 
- Entropy and Accuracy
- Interactions
- Markov Chain Monte Carlo
- Generalized Linear Models



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