Bayesian modeling is a statistical methodology that employs Bayes' theorem to formally update the probability of hypotheses or model parameters as new data become available. It is characterized by its principled incorporation of prior knowledge and its capacity to provide a full probabilistic distribution for inferred quantities, thereby quantifying uncertainty comprehensively. This approach serves as a powerful framework for inference, prediction, and decision-making under uncertainty across diverse research domains.