$P(A|B) = P(A) \times P(B)$

$P(A|B) = \frac{P(A \cup B)}{P(B)}$

$P(A|B) = \frac{P(A \cap B)}{P(B)}$

$P(A|B) = \frac{P(B|A)P(B)}{P(A)}$

Update the probability of a hypothesis based on new evidence.

Sum the probabilities of mutually exclusive events.

Calculate the probability of two events occurring simultaneously.

Determine if two events are statistically independent.

Normal Distribution

Poisson Distribution

Binomial Distribution

Uniform Distribution

$P(A|B) = P(B)$

$P(A \cap B) = P(A) + P(B)$

$A \cap B = \emptyset$

$P(A \cap B) = P(A)P(B)$

Naive Bayes Classifier

Support Vector Machines

K-Nearest Neighbors

Decision Tree Learning

The Law of Total Probability

The Binomial Distribution

The Central Limit Theorem

Bayes' Theorem

Normal Distribution

Binomial Distribution

Poisson Distribution

False

True

False

True

The Poisson distribution for rare mutations

Conditional probability and Bayes' theorem

The normal distribution of genetic traits

Independence of events and basic probability multiplication

$P(A)P(B)P(C)$

$P(A|B)P(B|C)P(C)$

$P(A|B \cap C)P(B|C)P(C)$

$P(A) + P(B) + P(C)$

To guarantee a positive outcome for any decision.

To quantify the uncertainty associated with different outcomes.

To determine the personal biases of the decision-maker.

To eliminate all risks associated with a decision.