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The advantage of MCMC methods is that the complexity increases only linearly with the number of objects, in contrast to importance sampling, in which the complexity grows exponentially [27].
One way to improve the approximation is to use importance sampling in which several values of θ ∗ are drawn from the Normal approximation, and the values of R are computed and used in choosing one of the θ ∗ values (Rubin, 1987b, 2003).
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The use of importance sampling in such graphical models is problematic because it generates many useless zero weight samples which are rejected yielding an inefficient sampling process.
We embed the auxiliary importance sampler in a penalized maximum likelihood framework which produces more accurate and computationally efficient parameter estimates.
In the context of structural reliability, SIS can be applied to produce samples of an approximately optimal importance sampling density, which can then be used for estimating the sought probability.
The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function.
On the right is the importance sampled image, which does exhibit less variance.
An importance sampling technique is described which is based on theoretical considerations about the structure of multivariate integrands in domains having small probability content.
The conditions under which importance sampling is applicable in high dimensions are investigated, where the focus is put on the common case of standard Gaussian uncertain parameters.
A new interval importance sampling method is developed in this paper which applies importance sampling technique to the imprecise probability.
The optimal importance sampling distribution is given by (22), which, for the current scenario, breaks down to (32).
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