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One diagnostic often used to detect a failure of importance resampling is when many candidate values of are rejected due to low values of the importance ratio In this case the procedure will take a very long time, and to be useful a better approximation is certainly needed Here, the long run time indicates a problem, and letting it run longer may eventually yield suf cient sample size However, this can be very frustrating and time consuming from a practical point of view There is a danger here, though: if the approximation distribution entirely misses a range of values of that have posterior density systematically different from the rest Since the normal has support over ( , ), the potential for this problem to occur vanishes as the number of simulations grows. barcode crystal reports Barcode Font Encoder Formulas for Crystal Reports Tutorial Easily create barcodes in Crystal Reports using fonts without installing UFLs* or DLLs. ... 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Therefore, one check is to compute a very large number of simulations with an arti cially large variance matrix, such as the pseudovariance matrix multiplied by a positive factor, which we label F This works since obviously the coverage is more diffuse Like all related simulation procedures, it is impossible to cover the full continuum of values that can take, and the procedure can miss subtle features such as pinholes in the surface, very sharp ridges, or other eccentricities 693 Relevance to the Generalized Process The importance sampling procedure cannot be relied on completely in our case, since we know that the likelihood surface is nonstandard by de nition of the problem The normal approximation requires an invertible Hessian The key to extracting at least some information from the Hessian via the derived pseudovariance matrix is determining whether the problems are localized or, instead, affect all the parameters. 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If they are localized, or the problem can be reparameterized so that they are localized, some parameters effectively have in nite standard errors, or pairs of parameters have perfect correlations The suggestion here is to perform two diagnostics to detect these problems and to alter the reported standard errors or covariances accordingly For small numbers of parameters, using pro le plots of the posterior can be helpful, and trying to isolate the noninvertibility problem in a distinct set of parameters can be very valuable in trying to understand the problem To make the normal or t-approximation work more ef ciently, it is generally advisable to reparameterize so that the parameters are unbounded and approximately symmetric This strategy is pretty standard in this literature and normally makes the maximization routine work better. 2 3 . 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