- Introduction to Bayesian Reasoning

- Historical Perspective of Bayesian Reasoning

- Understanding Priors

- Implementing Priors

- Advanced Bayesian Inference

- Bayesian Networks

- Bayesian Data Analysis

- Introduction to Bayesian Software

- Handling Complex Bayesian Models

- Bayesian Perspective on Learning

- Case Study: Bayesian Methods in Finance

- Case Study: Bayesian Methods in Healthcare

- Wrap Up & Real World Bayesian Applications

British mathematician and Presbyterian minister (*1701 – †1761).

Bayesian reasoning is a method of thought (named for Thomas Bayes) that deals with probability inference. It is a way of updating probabilities of hypotheses when given evidence. It follows the principle of the Bayes' theorem, which is fundamental to Bayesian statistics and Bayesian machine learning models.

At its core, Bayesian reasoning is a way of understanding the world and making decisions based on probabilistic inference. It is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.

Bayesian reasoning is not just a statistical technique. It is a comprehensive philosophical approach to understanding uncertainty and making sound decisions under uncertainty. It is a way of approaching any problem that involves making a decision under uncertainty.

The basic principle of Bayesian reasoning is to adjust your existing beliefs about the world based on new evidence. This is done by applying Bayes' theorem, which mathematically describes how to update probabilities based on new data.

In Bayesian reasoning, you start with a prior belief, which is your initial assumption about the world. Then, as you gather new evidence, you update your belief to form a posterior belief. The strength of your updated belief depends on the strength of your initial belief and the strength of the new evidence.

The key difference between Bayesian reasoning and other forms of reasoning lies in how they handle uncertainty. Traditional forms of reasoning, such as deductive or inductive reasoning, often deal with certainties. They start with premises and apply logical rules to arrive at a certain conclusion.

On the other hand, Bayesian reasoning deals with probabilities. It acknowledges that the world is uncertain and that we can only make probabilistic statements about it. Instead of starting with premises and arriving at conclusions, Bayesian reasoning starts with an initial belief and updates that belief based on new evidence.

In conclusion, Bayesian reasoning is a powerful tool for understanding the world and making decisions under uncertainty. It is a method of thought that deals with updating probabilities based on new evidence. By understanding Bayesian reasoning, we can make more informed and rational decisions in our everyday lives.