Probability model

Definition

A probability model is a mathematical representation of a random phenomenon, consisting of the sample space, events within the sample space, and probabilities associated with each event. It is used to predict outcomes and understand the likelihood of various results.

5 Must Know Facts For Your Next Test

1. A probability model includes both discrete and continuous types.
2. The sample space in a probability model represents all possible outcomes.
3. Events are subsets of the sample space and can include one or more outcomes.
4. Probabilities in a model must satisfy the axioms of probability: non-negativity, normalization, and additivity.
5. Conditional probability can be derived from a probability model to understand dependent events.

Review Questions

• What are the components of a probability model?
• How do you ensure that probabilities assigned in a probability model are valid?
• What is the difference between discrete and continuous probability models?

Related terms

Sample Space: The set of all possible outcomes in a probability experiment.

Event: A subset of the sample space; it consists of one or more outcomes.

Conditional Probability: $P(A|B)$ represents the likelihood of event A occurring given that event B has occurred.