Introduction
Bayesian statistics is a branch of statistics that relies on subjective probabilities to draw conclusions and learn from data. Basically, Bayes’ theorem is used to predict outcomes using prior information about an event, and one’s inferences about parameters are updated with each piece of evidence or data point.
Bayes’ Theorem:
Let A and B be events and P(B) != 0, then the probability of event A occurring given B is
P(A|B) =P(B|A)P(A)/P(B)
where P(A) and P(B) are probabilities of the event occurring independently
Additional information
- What is “Bayesian” Statistical Inference? (a blog post)
- What is Bayesian statistics? (a primer published in Nature Biotech)
- Introduction to Bayesian Linear Regression (an article from Towards Data Science)
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