Аннотация
Importance weighting is a convenient general way to adjust for draws from the
wrong distribution, but the resulting ratio estimate can be noisy when the
importance weights have a heavy right tail, as routinely occurs when there are
aspects of the target distribution not well captured by the approximating
distribution. More stable estimates can be obtained by truncating the
importance ratios. Here we present a new method for stabilizing importance
weights using a generalized Pareto distribution fit to the upper tail of the
distribution of the simulated importance ratios. The method includes stabilized
effective sample estimates, Monte Carlo error estimates and convergence
diagnostics.
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