Bayes' Rule Explained For Beginners

Bayes' Rule Explained For Beginners. Bayes' Rule has numerous applications, from statistical analysis to machine learning. Bayes' Rule can answer a variety of probability questions, which help us (and machines) understand the complex world we live in. This article will explain Bayes' Rule in plain language.

Bayes' Rule is the most important rule in data science. It is the mathematical rule that describes how to update a belief, given some evidence. In other words – it describes the act of learning.

The equation itself is not too complex:

The equation: Posterior = Prior x (Likelihood over Marginal probability)

There are four parts:

• Posterior probability (updated probability after the evidence is considered)
• Prior probability (the probability before the evidence is considered)
• Likelihood (probability of the evidence, given the belief is true)
• Marginal probability (probability of the evidence, under any circumstance)

Bayes' Rule can answer a variety of probability questions, which help us (and machines) understand the complex world we live in.

It is named after Thomas Bayes, an 18th century English theologian and mathematician. Bayes originally wrote about the concept, but it did not receive much attention during his lifetime.

French mathematician Pierre-Simon Laplace independently published the rule in his 1814 work Essai philosophique sur les probabilités.

Today, Bayes' Rule has numerous applications, from statistical analysis to machine learning.

This article will explain Bayes' Rule in plain language.

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