Probability & Simulation Lab — Monte Carlo, Sampling | GetCalcMaster
Run Monte Carlo simulations, sample distributions, and estimate probabilities. Inspect uncertainty and convergence, then export experiments into the notebook.
Probability & Simulation Lab
This lab is for simulation-first probability: generate samples, estimate probabilities, and build intuition for uncertainty. It’s especially useful when analytic solutions are hard or when you want to validate an analytic derivation empirically.
Open Probability & Simulation Lab
What you can do
- Sample common distributions and visualize summary stats.
- Run Monte Carlo experiments to estimate probabilities and expectations.
- Compare estimates as sample size increases (convergence intuition).
Verification pattern
A strong workflow is to do both analytic and simulation checks:
- Derive an expression in the notebook (or verify with CAS).
- Simulate the same quantity and see if the estimate matches within expected error.
- Export parameters, seeds, and results into the notebook.
FAQ
How many samples do I need?
It depends on variance and the precision you need. Increase sample size until estimates stabilize and confidence intervals are acceptable for your use case.
Why do Monte Carlo estimates “wiggle”?
Random sampling introduces noise. The estimate typically converges as sample size grows, but you should quantify uncertainty rather than expecting a fixed value immediately.
Can I reproduce a simulation?
Yes — store the configuration in the notebook, and use a fixed random seed when available to reproduce the same run.