Re:How to write better scientific code in Python?
by tgb on Saturday February 19, 2022 @03:48PM
(#30400405)
Well the integral doesn’t work for a Cauchy distribution either so that seems a little unfair for criticism. And this kind of procedure is not unusual for more complex distributions that have no well-understood density function to integrate, like the results a physics simulation, aren’t they?
Re:How to write better scientific code in Python?
by ww_wpg on Saturday February 19, 2022 @04:14PM
(#30400587)
> You cannot calculate the EV of a random variable X by taking the mean of random samples drawn from the distribution of X
My understanding of statistics is rudimentary so forgive me but doesn't the sample mean of a normally distributed variable tend towards the expected value for the population?
Re:How to write better scientific code in Python?
by mturmon on Saturday February 19, 2022 @06:05PM
(#30401384)
The sample mean of a Normal RV does tend towards the population expected value, sure.
The GP comment is talking about a Cauchy RV, which has heavy tails. So it has enough probability mass at large values that the expected value is infinite. Discarding constant scale factors, in this case:
E[X] = Int{0..infty} x * p(x) dx
= Int{0..infty} x * (1/x^2) dx
= Int{0..infty} (1/x) dx
= +infty
Re:How to write better scientific code in Python?
by vitorsr on Saturday February 19, 2022 @07:01PM
(#30401783)
The uncomfortable truth is that merely subscribing to the tenets of current software development “best practices” does not compensate for limitations in knowledge of applied mathematical sciences.
You cannot calculate the EV of a random variable X by taking the mean of random samples drawn from the distribution of X (e.g. try it with the Cauchy distribution—see if you get something approaching the actual EV). The only thing they accomplished is building up a very convoluted way of calculating a sample mean—which is already trivial with Numpy or just standard Python. Why do this? I’m perplexed.
This is the issue with software engineers writing scientific code—they often flagrantly misunderstand basic mathematical definitions, and then obfuscate this misunderstanding with their “pure” and “robust” code.
Sorry for the harsh criticism, but I’m tired of seeing this kind of thing.