Proof: Law of total probability
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The Book of Statistical Proofs ▷
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Probability theory ▷
Probability axioms ▷
Law of total probability
Metadata: ID: P248 | shortcut: prob-tot | author: JoramSoch | date: 2021-08-08, 03:56.
Theorem: Let $A$ be a subset of sample space $\Omega$ and let $B_1, \ldots, B_n$ be finite or countably infinite partition of $\Omega$, such that $B_i \cap B_j = \emptyset$ for all $i \neq j$ and $\cup_i \, B_i = \Omega$. Then, the probability of the event $A$ is
\[\label{eq:prob-tot} P(A) = \sum_i P(A \cap B_i) \; .\]Proof: Because all $B_i$ are disjoint, sets $(A \cap B_i)$ are also disjoint:
\[\label{eq:B-disjoint} B_i \cap B_j = \emptyset \quad \Rightarrow \quad (A \cap B_i) \cap (A \cap B_j) = A \cap (B_i \cap B_j) = A \cap \emptyset = \emptyset \; .\]Because the $B_i$ are exhaustive, the sets $(A \cap B_i)$ are also exhaustive:
\[\label{eq:B-exhaustive} \cup_i \, B_i = \Omega \quad \Rightarrow \quad \cup_i \, (A \cap B_i) = A \cap \left( \cup_i \, B_i \right) = A \cap \Omega = A \; .\]Thus, the third axiom of probability implies that
\[\label{eq:prob-tot-qed} P(A) = \sum_i P(A \cap B_i) \; .\]∎
Sources: - Alan Stuart & J. Keith Ord (1994): "Probability and Statistical Inference"; in: Kendall's Advanced Theory of Statistics, Vol. 1: Distribution Theory, p. 288, eq. (d); p. 289, eq. 8.7; URL: https://www.wiley.com/en-us/Kendall%27s+Advanced+Theory+of+Statistics%2C+3+Volumes%2C+Set%2C+6th+Edition-p-9780470669549.
- Wikipedia (2021): "Law of total probability"; in: Wikipedia, the free encyclopedia, retrieved on 2021-08-08; URL: https://en.wikipedia.org/wiki/Law_of_total_probability#Statement.
Metadata: ID: P248 | shortcut: prob-tot | author: JoramSoch | date: 2021-08-08, 03:56.