Lagmental Vicfred

Conditional Probability Renormalizes an Event by Vicfred

Inside an event B of positive probability, probabilities are divided by P(B). Keeping the exact identity in view prevents the geometric or probabilistic intuition from drifting.

Statement

A probability space \((\Omega,\mathcal F,\mathbf P)\) separates outcomes, measurable events, and their probabilities. A random variable \(X:\Omega\to\mathbf R\) must be measurable.

$$ \mathbf P(B)>0 $$

The first display fixes the mathematical data. I label it \(\mathsf{data}\) mentally, while the next is the \(\mathsf{claim}\); the bridge between them is the displayed \(\Longrightarrow\), not an automatic implication.

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

Worked algebra

A worked instance is useful here because it exposes every index that the compressed statement hides.

$$ \begin{array}{c|cc}&B&B^c\\\hline A&\mathbf P(A\cap B)&\mathbf P(A\cap B^c)\\A^c&\mathbf P(A^c\cap B)&\mathbf P(A^c\cap B^c)\end{array} $$

Conceptual compression

The two-row display is also a debugging tool: if the conclusion changes when only notation changes, some hidden choice has entered the argument.

$$ \begin{aligned} \mathsf{D}\;&:\quad \mathbf P(B)>0,\\[5pt] \mathsf{C}\;&:\quad \mathbf P(A\mid B)=\frac{\mathbf P(A\cap B)}{\mathbf P(B)}. \end{aligned} $$

Caveat

Conditioning on a probability-zero event cannot be done by naïvely dividing. Conditional densities and regular conditional probabilities require additional structure.

$$ \boxed{\begin{gathered} \text{compact conclusion}\\[-2pt] \mathbf P(A\mid B)=\frac{\mathbf P(A\cap B)}{\mathbf P(B)} \end{gathered}} $$

The important habit is to remember what was fixed before the calculation began and what was proved only afterward. The final display preserves that order.

This article was posted on Fri 23 April 2010. Facts and circumstances may have changed since publication.
Please contact me before jumping to conclusions if something seems wrong or unclear.