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通过模拟计算经济模型的分布AI 解读

We study a Monte Carlo algorithm for computing marginal and stationary densities of stochastic models with the Markov property, establishing global asymptotic normality and OP(n–1/2) convergence. Asymptotic normality is used to derive error bounds in terms of the distribution of the norm deviation.

通过模拟计算经济模型的分布AI 解读

We study a Monte Carlo algorithm for computing marginal and stationary densities of stochastic models with the Markov property, establishing global asymptotic normality and OP(n–1/2) convergence. Asymptotic normality is used to derive error bounds in terms of the distribution of the norm deviation.