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Bayes Theorem · cho.sh

Bayes Theorem

Bayes Theorem

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In Probability,

if $\{H_k\}$ partitions $\Omega$ then

P (H_{j} ∣ E) = \frac{P (H_{j} \cap E)}{P (E)} = \frac{P (H_{j}) P (E ∣ H_{j}}{}

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\sum_{k} P (H_{k}) P (E ∣ H_{k})

P(H_j | E) = {P(H_j \cap E) \over P(E)} = {{P(H_j) P(E|H_j)} \over {\sum\limits_{k} P(H_k) P(E|H_k)}}

P (H_{j} ∣ E) = \frac{P ( H _{j} \cap E )}{P ( E )} = k \sum P ( H _{k} ) P ( E ∣ H _{k} )

$H_j$ is posterior in this case.

The Odds form of Bayes Theorem is

O(H|E) = O(H) {P(E|H) \over P(E|H^C)}

If ${H_k}$ partitions $\Omega$ then

$P(H_j | E) = {{P(E|H_k) P(H_k)} \over {\sum\limits_{j} P(E|H_j) P(H_j)}}$

P(Hj)P(E∣Hj)

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