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Equation 8 · AI for Science and Medicine in Practice: An Advanced Technical Guide

What does this equation mean?

P(at least one success in k)=1−(1−p)k,P(\text{at least one success in } k) = 1 - (1-p)^{k},

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Inputs and operations1 - (1-p)^k
Result or conditionP(at least one success in k)
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This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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PP

Symbol P

P is part of the quantity the equation computes from the expression on the right.

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kk

Symbol k

k is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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pp

Symbol p

the probability.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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subtraction

subtraction

Subtract the following term or group from the preceding one. A leading minus marks a negative quantity.

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superscript

superscript

A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.

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How to interpret it

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What the article says around this equation

Batch size interacts with the value of parallel experiments in a way worth costing out explicitly. If a single proposed experiment succeeds with probability p , and a batch of k proposals were independent, the probability that at least one succeeds is P(at least one success in k)=1−(1−p)kP(\text{at least one success in } k) = 1 - (1-p)^{k}. which is why both systems ran in batches — parallel throughput converts a low per-attempt success probability into a high per-batch one. Two caveats matter in practice: proposals drawn from the same model on the same pool are correlated, so realised batch success rates fall short of this bound, and a larger batch is only worth its cost if throughput, not sample count, is the bottleneck actually being relieved.

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