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Monte Carlo simulation uses random sampling to approximate solutions to problems that are too complex for analytical formulas, making it indispensable in finance, physics, engineering, and statistics. This simulator starts with the classic π-estimation experiment—randomly placing points in a square and counting how many fall inside the inscribed circle—then extends to numerical integration, risk modeling, and probability estimation. Watching the approximation converge as sample count grows makes the law of large numbers a lived experience rather than a theorem.
Monte Carlo simulation · random sampling · law of large numbers · numerical integration · convergence · probability estimation
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