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| Both sides previous revision Previous revision Next revision | Previous revision | ||
| kb:hypothesis_testing [2021-05-11 20:04] – jaeyoung | kb:hypothesis_testing [2024-04-30 04:03] (current) – external edit 127.0.0.1 | ||
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| Since we are just comparing $P(H_0|R = r)$ and $P(H_1|R = r)$, we can cancel out the $f_R(r)$ on both sides, so it is equivalent to comparing $P(H_0) f_{R|H}(r|H_0)$ and $P(H_1) f_{R|H}(r|H_1)$: | Since we are just comparing $P(H_0|R = r)$ and $P(H_1|R = r)$, we can cancel out the $f_R(r)$ on both sides, so it is equivalent to comparing $P(H_0) f_{R|H}(r|H_0)$ and $P(H_1) f_{R|H}(r|H_1)$: | ||
| - | * If $P(H_0) f_{R|H}(r|H_0) > P(H_1) f_{R|H}(r|H_1)$, then announce $' | + | * If $P(H_0) f_{R|H}(r|H_0) > P(H_0) f_{R|H}(r|H_0)$, then announce $' |
| * If $P(H_0) f_{R|H}(r|H_0) < P(H_1) f_{R|H}(r|H_1)$, | * If $P(H_0) f_{R|H}(r|H_0) < P(H_1) f_{R|H}(r|H_1)$, | ||
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| The likelihood ratio $\Lambda(r)$ is defined as: | The likelihood ratio $\Lambda(r)$ is defined as: | ||
| - | $$ \Lambda(r) = \frac{P(H_1) | + | $$ \Lambda(r) = \frac{f_{R|H}(r|H_1)}{f_{R|H}(r|H_0)} $$ |
| We can compare this likelihood ratio to the threshold $\eta$, which is the ratio between the a priori probabilities: | We can compare this likelihood ratio to the threshold $\eta$, which is the ratio between the a priori probabilities: | ||