Simulation and the Monte Carlo Method¶
Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo Method. Wiley.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Collision Simulation Grid
- Its strength is honesty about messy reality: it is the only tool in the archetype that can price collision risk when draws are human-chosen, biased, dependent, or partitioned, and it does so with a quantified uncertainty band rather than a false-precision point.
This sourceUses Monte Carlo simulation to estimate uncertain probabilities and quantities from repeated random sampling, with sampling error that can be expressed through confidence bounds.
- Its strength is honesty about messy reality: it is the only tool in the archetype that can price collision risk when draws are human-chosen, biased, dependent, or partitioned, and it does so with a quantified uncertainty band rather than a false-precision point.
- Probabilistic Risk Simulation
- This is the rare-event problem that importance sampling exists to address.
This sourcePresents importance sampling as a method for estimating rare-event probabilities efficiently.
- This is the rare-event problem that importance sampling exists to address.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Not recorded. Neither this nor any other of the 2 citations of this work carries a recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:6ba7cd510b61 · see in the full table