Statistical uncertainties Monte Carlo

Click For Summary
SUMMARY

This discussion focuses on calculating statistical uncertainties in Monte Carlo simulations, specifically when comparing distributions generated from different event counts (NA and NB). It is established that statistical uncertainty can be estimated using the square root of the number of events, following the principles of Poisson distributed noise. The accuracy of these estimates is contingent upon the sizes of NA and NB, with larger counts yielding more reliable results. Additionally, the method of representing distributions, such as particles per bin, influences the uncertainty calculations.

PREREQUISITES
  • Understanding of Monte Carlo simulations and their applications
  • Familiarity with statistical concepts, particularly Poisson distribution
  • Knowledge of error propagation in statistical analysis
  • Experience with data representation techniques in simulations
NEXT STEPS
  • Study Poisson distribution and its implications for statistical uncertainty
  • Learn about error propagation methods in Monte Carlo simulations
  • Explore techniques for optimizing event counts in simulations
  • Investigate data visualization methods for representing simulation results
USEFUL FOR

This discussion is beneficial for physicists, statisticians, and researchers involved in Monte Carlo simulations, particularly those interested in understanding and calculating statistical uncertainties in their results.

Lian1985
Messages
1
Reaction score
0
Hi!

I'm newbie in Monte Carlo. My question concerns the statistical uncertainties of my simulations.

Let's say that the result of my Monte Carlo is a certain distribution A (e.g. number of particles as a function of the depth in a target) and I have run NA events to generate that distribution. Then, I run the same code with NB events and I obtain the distribution B.
Can I calculate the statistical uncertainties of my results based on only on these information?

Thank you for your help!
 
Physics news on Phys.org
You can get an estimate of statistical uncertainty, although there will be question of how good it is. It will depend partly on how large NA and NB are - the larger the better.

Suggestion: move to the statistics forum.
 
At the very least, you will be limited by shot noise due to quantized number of particles. Typically, this gives you a Poisson distributed noise, with error which scales as sqrt(N), with N the number of particles. Details will depend on what your code is doing. If your distribution is represented as a number of particles per bin, then the error is at least sqrt(number of particles in that bin) unless some averaging was done in the code.
 

Similar threads

Undergrad Monte Carlo for uncertainty estimation
  • · Replies 2 ·
Replies
2
Views
2K
Puzzled about the non-teaching of Monte Carlo method(s) for error analysis
  • · Replies 67 ·
3
Replies
67
Views
7K
Undergrad Confused about using Monte Carlo for error estimation
  • · Replies 12 ·
Replies
12
Views
3K
Help with Monte Carlo Wang-Landau JDoS
  • · Replies 1 ·
Replies
1
Views
2K
C/C++ Polymer simulation source code in C++ and Python
  • · Replies 1 ·
Replies
1
Views
2K
A question about an expression definition in Monte Carlo
  • · Replies 3 ·
Replies
3
Views
2K
Graduate Question about data & Monte Carlo statistical uncertainties
  • · Replies 2 ·
Replies
2
Views
1K
Graduate Monte Carlo to compute uncertainties, why report the mean and SD?
  • · Replies 2 ·
Replies
2
Views
1K
Undergrad Monte Carlo financial simulation
  • · Replies 2 ·
Replies
2
Views
1K
Graduate Where to start with path integral Monte Carlo?
  • · Replies 1 ·
Replies
1
Views
2K