This webinar offers an accessible introduction to Bayesian reasoning and its role in making predictions under uncertainty.
We will explore how Bayesian reasoning provides a structured framework for forming predictions, evaluating uncertainty, and updating our beliefs as new information becomes available.
The session will begin with the core concepts of prior and posterior beliefs and conditional probability, before moving on to Bayes’ theorem and its implications for reasoning and prediction.
Throughout the webinar, these ideas will be illustrated through clear and practical examples, showing how Bayesian reasoning can help us interpret evidence, refine our expectations, and develop a more disciplined understanding of an uncertain world.