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 is an approach to inferential statistics where a researcher specifies the prior probability that the null hypothesis and alternative hypotheses are true before conducting a study. Once the study is conducted, the probabilities are updated based on the newly collected data. This approach offers a potential alternative to traditional null hypothesis testing by providing a logical framework for quantifying uncertainty using probability distributions.

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



Concepts in Bayesian Statistics

When using Bayesian statistics, what must a researcher explicitly specify before conducting a study?

In an approach to inferential statistics that begins by assigning initial probabilities to competing hypotheses, those probability estimates remain fixed and do not change once new data from a study are collected.

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