To conduct a Monte Carlo Simulation you need to be able to write down the formula for the distribution of the outcomes you are most interest in.

Question

BlackTom AI · Accepted Answer

When evaluating a claim about Monte Carlo Simulation, it helps to recall the core requirement: you need a mathematical description of the distribution of outcomes you want to study so you can generate random samples from it. 
Option 1: True. This is accurate because Monte Carlo methods rely on simulating many independent draws from the target distribution to approximate quantities of interest, such as expectations or probabilities. Having the distribution (or a way to sample from it) is essential to perform the simulations properly. 
Option 2: False. This would be incorrect because, in contrast, if you do not have a way to express the distribution of outcomes or to sample from it, you cannot construct the random scenarios needed for the Monte Carlo estimation. Without a defined distribution, the simulated results would lack a principled basis and the method would not be well-founded. 
In sum, the statement asserts that a distribution formula is necessary to implement Monte Carlo simulations, and the reasoning shows why that necessity holds both in theory (sampling from the distribution drives the simulation) and in practice (without a distribution, you cannot generate representative samples).

SP25 RMI 660 001 Engaged Learning 16: Intro to Monte Carlo Simulations

To conduct a Monte Carlo Simulation you need to be able to write down the formula for the distribution of the outcomes you are most interest in.

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