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I agree, but understanding probability distributions is actually key to this simulation that they've performed. The results would differ, greatly, by running it
by carbocation 16y ago
I agree, but understanding probability distributions is actually key to this simulation that they've performed. The results would differ, greatly, by running it with a uniform vs normal vs binomial distribution, or by tuning their parameters.
- jonp 16y agoI was thinking that they probably just simulated each individual patient by picking a random number and seeing if it's less than the death rate. So the number of deaths in a hospital is just sum([random()<deathRate for i in range(numPeople)]) and they can do the analysis without needing to know what a Bernouilli or Binomial distribution is, even though these both feature in the problem.
- carbocation 16y agoAgain, the "random number" comes from a distribution. You can have a random number from the binomial, normal, etc distribution, and the output will look quite different. Also, there are clearly bounds on the distribution. Etc. They absolutely do need to understand how their pseudorandom number generator works in order to understand the properties of their simulation.
- jonp 16y agoI can't tell if we're disagreeing or just at cross purposes. In the code above the random number is uniform(0,1), the standard random number in most languages. So each individual death is Bernouilli, although you don't need to know the word Bernouilli. Then this leads to a binomial distribution for a hospital as a whole. But the binomial assumption doesn't need to be known or explicitly built into the model. It's effectively an emergent property of the individual deaths.