About Machine Learning ( Part 7: Artificial Neural Network )
Bayes’ theorem
$$
P(y|X) = \frac{P(X|y) P(y)}{P(X)}
$$
where:
- $P(y|X)$: Posterior probability of class $y$ given input $X$.
- $P(X|y)$: Likelihood of seeing $X$ if the class is $y$.
- $P(y)$: Prior probability of class $y$.
- $P(X)$: Total probability of $X$ (normalization factor).
Bayes Network (Bayesian Network, BN)
A Bayesian network (BN) is a graphical model representing probabilistic dependencies between variables. It consists of:
- Nodes: Represent variables (e.g., symptoms, diseases).
- Edges: Represent conditional dependencies.
