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Recently, Rue et al.[ 37] proposed an approximate alternative for parameter estimation in a subclass of Bayesian hierarchical models, the so-called latent Gaussian models.
To generate an approximate alternative, we set initial bounds on the parameter space Λ such that N A ∈ {10, 10}, N 0 ∈ {10, 5×10}, and τ ∈ {0, 5×10}.
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For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions.
We show that given the MAP for a belief network and evidence set, or the family of MAPs if the optimal is not unique, it remains NP-hard to find, or approximate, alternative next-best explanations.
The dummy could be set up to approximate alternative single-degree-of-freedom and two-degree-of-freedom apparent mass models of the seated human body by varying motion feedback parameters.
Since importance sampling is not an approximate method, but an alternative means of simulation, the predicted long-term fatigue damage is unbiased.
This paper explores an approximate LCA of product design alternatives represented by solid models in a collaborative design environment.
As an alternative, we employ an approximate solution strategy.
An alternative variant is to select an Approximate Singular Value Decomposition of the Actual Degrees of Disequilibrium (ASVDADD).
Meanwhile, numerical methods turn out to be an alternative way of attaining an approximate solution of FDEs.
In systems biology, an approximate Bayesian computing approach to selecting between alternative models of the JAK-STAT pathway demonstrated strong evidence in favour of one model [ 1].
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