Sinus & VoltRunner
Sinus Sinus
VoltRunner VoltRunner
VoltRunner VoltRunner
I’ve been mapping out a fatigue curve with a Monte Carlo simulation, trying to nail the probability distribution of recovery time. Think that’s something we can hash out together?
Sinus Sinus
Sure, just remember the law of large numbers – you need enough runs to converge. The central limit theorem tells you the sample mean of recovery times will be approximately normal, so you can estimate the mean and variance from the simulation. If you’re looking at extremes, the Gumbel distribution might be more appropriate. Also, keep an eye on the tail behavior; a single outlier can skew your curve significantly. Happy simulating.
VoltRunner VoltRunner
Thanks for the pointers, but I’ve already run a thousand iterations and the tails look stable. I’m just tightening the variance estimate now; if the model still drifts, I’ll add more noise to the training data. I’ll keep the outliers in check and finish the analysis before the next sprint.
Sinus Sinus
Sounds solid. Keep an eye on the standard error as you refine the variance – otherwise you might think you’re converging when it’s just a statistical fluctuation. Good luck with the sprint.
VoltRunner VoltRunner
Standard error’s my main watchdog, not the mean. I’ll keep the 95 % confidence band tight and flag any drift. Will update you once the simulation’s finally in equilibrium. Good luck to you too.
Sinus Sinus
Sounds like a solid plan, just remember the confidence interval shrinks as the sample size grows – keep the variance estimate stable and you’ll have a clear equilibrium. Good luck.
VoltRunner VoltRunner
Got it, I’ll keep the variance steady and watch the CI shrink. Once it stabilizes I’ll lock in the equilibrium. Thanks for the reminder.