Model Comparison Techniques
Bayesian Model Selection typically involves comparing models using specific statistical methods that quantify how well each model performs. The most commonly used techniques include:
- Bayes Factor: A ratio of the posterior probabilities of two models, providing evidence in favor of one model over another.
- Bayesian Information Criterion (BIC): While not purely Bayesian (they stem from a frequentist perspective), these criteria are often used in a Bayesian context to approximate Bayes factors with easier computation.
- Deviance Information Criterion (DIC) and Widely Applicable Information Criterion (WAIC): These are more directly Bayesian and focus on the trade-off between model complexity and goodness of fit.
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
Contact Us