On 14/01/2019 20:11, duncan smith wrote:
> Hello,
> Just checking to see if anyone has attacked this problem before
> for cases where the population size is unfeasibly large. i.e. The number
> of categories is manageable, but the sum of the frequencies, N,
> precludes simple solutions such
On 15/01/2019 17:59, Ian Hobson wrote:
> Hi,
>
> If I understand your problem you can do it in two passes through the
> population.
>
The thing is that I start with the population histogram and I want to
generate a sample histogram. The population itself is too large to deal
with each
Hi,
If I understand your problem you can do it in two passes through the
population.
First, however, lets work through taking a sample of 2 from 7 to
demonstrate the method.
Take the first element with a probability of 2/7. (Note 1).
If you took it, you only want 1 more, so the probability
On 15/01/2019 02:41, Spencer Graves wrote:
>
>
> On 2019-01-14 18:40, duncan smith wrote:
>> On 14/01/2019 22:59, Gregory Ewing wrote:
>>> duncan smith wrote:
Hello,
Just checking to see if anyone has attacked this problem before
for cases where the population size is
On 2019-01-14 18:40, duncan smith wrote:
On 14/01/2019 22:59, Gregory Ewing wrote:
duncan smith wrote:
Hello,
Just checking to see if anyone has attacked this problem before
for cases where the population size is unfeasibly large.
The fastest way I know of is to create a list of
On 14/01/2019 22:59, Gregory Ewing wrote:
> duncan smith wrote:
>> Hello,
>> Just checking to see if anyone has attacked this problem before
>> for cases where the population size is unfeasibly large.
>
> The fastest way I know of is to create a list of cumulative
> frequencies, then
duncan smith wrote:
Hello,
Just checking to see if anyone has attacked this problem before
for cases where the population size is unfeasibly large.
The fastest way I know of is to create a list of cumulative
frequencies, then generate uniformly distributed numbers and
use a binary search
Hello,
Just checking to see if anyone has attacked this problem before
for cases where the population size is unfeasibly large. i.e. The number
of categories is manageable, but the sum of the frequencies, N,
precludes simple solutions such as creating a list, shuffling it and
using the first
