Bayesian Inference in Data Science
What is Bayesian Inference?
In statistics and data science, Bayesian inference is a method of updating probabilities as new data becomes available. It applies Bayes’ theorem to combine prior knowledge with observed evidence, producing a posterior distribution that reflects updated beliefs. Bayesian inference treats probability as a measure of uncertainty, not only as a long-run frequency. It is widely used to model uncertainty, integrate prior information, and adapt conclusions as new data arrive.
About the Author
Adam Boros
Adam studied at the University of Toronto, Faculty of Medicine for his MSc and PhD in Developmental Physiology, complemented by an Honours BSc specializing in Biomedical Research from Queen's University. His extensive clinical and research background in women’s health at Mount Sinai Hospital includes significant contributions to initiatives to improve patient comfort, mental health outcomes, and cognitive care. His work has focused on understanding physiological responses and developing practical, patient-centered approaches to enhance well-being. When Adam isn’t working, you can find him playing jazz piano or cooking something adventurous in the kitchen.
