Key Components of Bayesian Statistics
The key components of this framework are:
- Prior Probability (Prior): This represents the belief about the model before seeing the data.
- Likelihood: The probability of the data given the model.
- Posterior Probability: The probability of the model given the data, obtained by updating the prior with the likelihood using Bayes’ theorem.
Bayesian Model Selection
Bayesian Model Selection is an essential statistical method used in the selection of models for data analysis. Rooted in Bayesian statistics, this approach evaluates a set of statistical models to identify the one that best fits the data according to Bayesian principles. The approach is characterized by its use of probability distributions rather than point estimates, providing a robust framework for dealing with uncertainty in model selection.
Table of Content
- What is the Bayesian Model Selection?
- Bayesian Inference
- Key Components of Bayesian Statistics
- Prior and Posterior Probability
- Prior Probability
- Posterior Probability
- Model Comparison Techniques
- Bayesian Factor (BF)
- Bayesian Information Criterion (BIC)
- Advantages of Bayesian Model Selection
- Conclusion