Probability Formulas, Symbols & Notations - Marginal, Joint, & Conditional Probabilities
The Organic Chemistry Tutor・2 minutes read
Marginal probability calculates the probability of a single event occurring independently, while conditional probability determines the likelihood of an event happening given that another event has already occurred. Understanding these concepts is essential for differentiating between events occurring together simultaneously or in a specific order.
Insights
- Marginal probability is the likelihood of a single event happening on its own, without considering other events, calculated by dividing successful outcomes by total possible outcomes.
- Conditional probability involves the likelihood of an event occurring given that another event has already happened, determined by the probability of A given B times the probability of B. This concept is essential in distinguishing between events happening together simultaneously or in a specific order.
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Recent questions
What is marginal probability?
Marginal probability is the probability of a single event occurring independently of other events, calculated as the number of successful outcomes divided by the total possible outcomes.
How is conditional probability calculated?
Conditional probability is the probability of an event occurring given that another event has already occurred, calculated as the probability of A given B times the probability of B.
What is the difference between independent and dependent events?
Independent events have the probability of A given B equal to the probability of A, while dependent events are where the outcome of one event affects the probability of the other event.
What is the formula for Bayes' theorem?
Bayes' theorem is calculated as the probability of A given B equals the probability of B given A times the probability of A, divided by the probability of B.
How is negation probability calculated?
Negation probability, or the complement of an event, is calculated by subtracting the probability of the event from 1. This concept is crucial in understanding probabilities within a sample space.
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