Probability, Data Science, Machine Learning, Data Visualization.
Bayesian estimation combines prior information with observed data to update the probability distribution over the parameters. This approach is crucial in Bayesian statistics and has applications in model parameter tuning and Bayesian machine learning, where uncertainty is explicitly represented.