Posterior Probability: Definition, Formula for Calculation

By
Adam Hayes
Full Bio

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

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Updated September 24, 2021
Reviewed by Margaret James

What Is a Posterior Probability?

A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. The posterior probability is calculated by updating the prior probability using Bayes' theorem. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred.

Key Takeaways

  • A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information.
  • The posterior probability is calculated by updating the prior probability using Bayes' theorem.
  • In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred.

Bayes' Theorem Formula

The formula to calculate a posterior probability of A occurring given that B occurred:

P ( A ∣ B ) = P ( A ∩ B ) P ( B ) = P ( A ) × P ( B ∣ A ) P ( B ) where: A , B = Events P ( B ∣ A ) = The probability of B occurring given that A is true P ( A )  and  P ( B ) = The probabilities of A occurring and B occurring independently of each other \begin{aligned}&P(A \mid B) = \frac{P(A \cap B)}{P(B)} = \frac{P(A) \times P(B \mid A)}{P(B)}\\&\textbf{where:}\\&A, B=\text{Events}\\&P(B \mid A)=\text{The probability of B occurring given that A}\\&\text{is true}\\&P(A) \text{ and }P(B)=\text{The probabilities of A occurring}\\&\text{and B occurring independently of each other}\end{aligned} ​P(A∣B)=P(B)P(A∩B)​=P(B)P(A)×P(B∣A)​where:A,B=EventsP(B∣A)=The probability of B occurring given that Ais trueP(A) and P(B)=The probabilities of A occurringand B occurring independently of each other​

The posterior probability is thus the resulting distribution, P(A|B).

What Does a Posterior Probability Tell You?

Bayes' theorem can be used in many applications, such as medicine, finance, and economics. In finance, Bayes' theorem can be used to update a previous belief once new information is obtained. Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account.

Posterior probability distributions should be a better reflection of the underlying truth of a data generating process than the prior probability since the posterior included more information. A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis.