app4_eqk_pred - Application Example 4 (Bayes' Theorem)...

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Application Example 4 (Bayes’ Theorem) EARTHQUAKE PREDICTION FROM IMPERFECT PREMONITORY SIGNS Ordinary people as well as seismologists have observed that, in some cases, major earthquakes occur shortly after certain anomalous events, which they then have claimed can be used for earthquake prediction. One of the earliest reported such premonitory signs is the anomalous behavior of animals. Recently, interest has shifted towards more objectively measurable phenomena such as geophysical anomalies, variations in groundwater level, and small changes in the topography near the causative earthquake fault. Mathematical models have also been developed, trying to establish theoretical links between such quantitative observables and the occurrence of large earthquakes. The main issue that determines the practical usefulness of these premonitory events is the accuracy with which predictions can be made. Accuracy can be quantified in terms of the following probabilities P 1|1 = P[earthquake occurs|earthquake is predicted] P 0|1 = P[earthquake does not occur|earthquake is predicted] = 1 - P 1|1 (1) P 0|0 = P[earthquake does not occur |earthquake is not predicted] P 1|0 = P[earthquake occurs|earthquake is not predicted] = 1 - P 0|0 For a perfect prediction system, P 1|1 = P 0|0 = 1 and P 0|1 = P 1|0 = 0.
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The probabilities in Eq. 1 depend on the strength of the association between the
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This note was uploaded on 02/27/2008 for the course PTE 461 taught by Professor Donhill during the Fall '07 term at USC.

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app4_eqk_pred - Application Example 4 (Bayes' Theorem)...

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