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With simple random sampling, every possible sample of size n has the same probability of being selected.
Choosing the elements from the population one at a time so that each element has the same probability of being selected will provide a simple random sample.
Tables of random numbers, or computer-generated random numbers, can be used to guarantee that each element has the same probability of being selected.
The latter has the same probability of success as the legal method.
In our case, each component has the same probability to be selected.
Since every element occurring in (mathscr{SP}_{n}) has the same probability, we have (p_{1}=p_{2}=1-p_{1}-p_{2}).
Most methods consider sampling only from a homogeneous population in which each animal has the same probability of becoming infected.
where (underline {ell }_{i} = U underline {h}_{i}) has the same probability density function as (underline {h}_{i}), i=2,…,K.
Assuming that each attack scenario has the same probability for occurrence, optimal system designs that account for the simulated possible attack scenarios are obtained.
Yet every single individual sequence of coin flips has the same probability (1/2^100) and the same multiplicity (one), therefore, must have the same value of entropy.
For NetInf, the edges in the network are viewed as identical; i.e., the connected source node has the same probability to infect the end node.
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