Monte Carlo concept
| Definition | Estimating a probability or an expectation as the fraction or average over many simulated runs, a sample proportion with standard error the square root of p(1 minus p) over N. A null model run many times is one. Primer S, equation S.21. |
|---|---|
| Example | 17 of 100 runs show the event, so the estimate is 0.17 with standard error the square root of 0.17 times 0.83 over 100, which is 0.038. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id monte-carlo, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | Book equation S.21. |
| Assumptions and scope | none |
| Prior art | none recorded |
| Evidence | none |
| Reviewed | semantic review 2026-09-09; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
Book equation S.21.
\[\hat p=\frac{\#\text{runs with the event}}{N},\qquad \operatorname{SE}(\hat p)=\sqrt{\frac{\hat p(1-\hat p)}{N}}.\]
Conditions
none
Ledger
none
First stated
Primer S section S.11 of Data Mining as Observation, added in draft 0.3 (2026-09-09) for the ECE 514 readers whose first courses are far behind. The idea is standard and TSK Appendix C covers it at length.
Measurements
none
Failures and corrections
none
Invariance envelope
none declared
Machine checked
none
Used in
Data Mining as Observation primer S.
Related
pseudo-random generator; null model; standard error; permutation test.
See also
none
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.