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== Introduction == | == Introduction == | ||
| + | In this slecture, we discuss Bayes' rule that is widely used for many different kinds of applications, especially, pattern recognition. Due to its simplicity and effectiveness, we can use the method in both discrete values case and continuous values case, and it also usually works well in multi-dimensional feature space. So, we will take a look at what the definition of Bayes' rule is, how it can be used for the classification task with examples, and how we can derive it in different cases. | ||
| + | |||
| + | == Bayes' Theorem == | ||
| + | Bayes' theorem is a probabilistic theory that can explain a relationship between the prior probability and the posterior probability of two random variables or events. | ||
Revision as of 06:35, 12 March 2014
Introduction
In this slecture, we discuss Bayes' rule that is widely used for many different kinds of applications, especially, pattern recognition. Due to its simplicity and effectiveness, we can use the method in both discrete values case and continuous values case, and it also usually works well in multi-dimensional feature space. So, we will take a look at what the definition of Bayes' rule is, how it can be used for the classification task with examples, and how we can derive it in different cases.
Bayes' Theorem
Bayes' theorem is a probabilistic theory that can explain a relationship between the prior probability and the posterior probability of two random variables or events.
