Bayesian reasoning has been used in a variety of fields and especially in the field of artificial intelligence and machine learning. In this little note, we have summed up Bayesian reasoning to three simple points.Read our article ‘Untangling Bayesian Reasoning‘ for a detailed explanation with a simple example.
- Bayesian probability depends on previous knowledge to come up with the probability that an event can occur.
- We use the individual probability (prior) and data in hand (evidence) to calculate the conditional probability (posterior).
- We can iterate this and increase its accuracy by iterating this calculation. The posterior is taken as the prior for the next iteration with the new data in hand.
- As more data comes into the picture more subjectivity from multiple angles gets in.
- This iterative method is difficult to use for events which do not repeat again and again. In such cases, we use hypothesize the probability of an event occurring.
- Calculating Bayesian probability assesses the strength of our belief and the strength changes with the addition of new information. It depends heavily on the input it gets from our senses and it is impossible that the evidence is a hundred percent accurate and has many imperfections.
As I said earlier, read our article ‘Untangling Bayesian Reasoning‘ to learn more with a super simple example.E-mail us at she@shedrivesdata.com to inspire our readers with your story – be it your success story or a lesson learned, share what you learned or send some love to a friend. We would love to hear from you!