# Neal P.'s A case study in non-centering for data augmentation: PDF

By Neal P.

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**Example text**

To give extra protection, some components may be duplicated and placed in parallel, so that only one of them need work; but for components in series, all must work or the system will fail. Consider the set-up shown in the figure on the left, where the numbers in the boxes are the probabilities that component will fail. 28. 352, so the original figure is equivalent to the new system on the right. 09856, or about 10%. 90144, call it 90%. 14 (Simpson's Paradox) Suppose that 200 of 1000 males, and 150 of 1000 females, in University A read Economics.

What is your new opinion about the chance that A has occurred? The notation for what you seek is P(AIB), read as "The probability of A, conditional upon B", or as "The probability of A, given B" . To see how this might relate to our previous ideas, consider a repeatable experiment with K equally likely outcomes. The probabilities of events A and B are then found by counting; how to assess the probability of A, given that B occurs? Plainly we can ignore all those experiments in which B does not occur.

1), let n, k be fixed, while M,N ---* 00 keeping the ratio M/(M + N) = P constant. 2 for the binomial distribution. Why is this not a surprise? 2. A majority verdict of 10-2 or better may be permitted in a jury trial. 9 of reaching a Guilty verdict, and decides independently, what is the probability the jury decides to convict? 3. Assume that within a given service game at tennis, successive points form Bernoulli trials with P = P(Server wins) > 1/2. Tennis rules say that the service game ends as soon as either player has won at least four points, and is at least two points ahead of the other.

### A case study in non-centering for data augmentation: Stochastic epidemics by Neal P.

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