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Bayesian Data Analysis is an approach to statistical modeling and inference based on Bayes’ theorem. It involves updating probability estimates for hypotheses as new data becomes available. R and Stan are commonly used tools for Bayesian Data Analysis. Below, I’ll provide an overview of Bayesian Data Analysis with R and Stan, including key concepts, tools, and resources.
1. Bayesian Data Analysis (BDA):
a. Bayesian Inference:
- Bayesian methods treat parameters as probability distributions, updating beliefs based on observed data using Bayes’ theorem.
b. Bayesian Model:
- A statistical model that incorporates prior knowledge and updates it with observed data to obtain a posterior distribution.
2. Stan:
- What is Stan? Stan is a probabilistic programming language for Bayesian statistical inference. It is particularly well-suited for complex models and is designed for efficiency and flexibility.
- Stan Language: Stan has its own language for expressing probabilistic models. It uses a syntax similar to mathematical notation, making it expressive and readable.
- Interfaces: RStan is an R interface to…
