Monte Carlo Expected Value Interview Questions
Monte Carlo expected value interview prep for sampling estimators, convergence intuition, error scaling, and simulation pitfalls.
Candidates preparing for quant research, coding, and probability discussions.
Monte Carlo estimator
A Monte Carlo expected value estimate averages simulated payoff samples from the model.
Law of large numbers
As the number of valid independent samples grows, the simulated average should move toward the model expected value.
Concrete example
To estimate the EV of a payoff from two random dice, simulate many pairs of rolls, compute the payoff for each pair, and average.
Error scaling
Monte Carlo error typically shrinks like one over the square root of sample count, so getting ten times more precision can require many more samples.
Implementation checks
Check the random sampler, payoff calculation, seed handling, and whether the simulation matches the exact rules of the prompt.
Common mistakes
Candidates often simulate the wrong process accurately. The model and payoff mapping matter more than the loop itself.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.