Bayesian analysis is a statistical technique based on Bayes' theorem, used for estimating unknown parameters or modeling uncertainty. Unlike classical statistics, Bayesian analysis integrates prior knowledge or assumptions about parameters into the analysis but updates these assumptions based on new data.
Typical software functions in the area of "Bayesian analysis" include:
Setting Priors: Specifying the prior distribution or assumptions about parameters based on available knowledge or expertise.
Data Analysis: Incorporating new data to update the prior distribution and compute posterior distributions of parameters.
Modeling Uncertainty: Computing confidence or credibility intervals for estimated parameters reflecting uncertainty.
Sensitivity Analysis: Analyzing the impact of changes in the prior distribution or new data on estimated parameters.
Visualization: Presenting results through graphs illustrating prior and posterior distributions, as well as confidence intervals.
Reporting: Generating reports or summaries of Bayesian analysis for decision-makers and stakeholders.