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For the multidimensional parameter case when the parameters may be partitioned in a block of parameters of interest and another block of nuisance parameters, Bernardo (1979) suggested an approach in three stages.
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In each of these cases one is seeking a point in a multidimensional parameter space that produces maximal perceived quality.
For a multidimensional parameter β, it is true in the following example.
This approach is especially helpful in multidimensional parameter spaces.
There are two possible multidimensional parameter extensions of the fBm.
There are two possible multidimensional parameter extensions of the subfractional Brownian motion.
Particle swarm optimization [2] is a stochastic, population-based evolutionary algorithm for problem solving in complex multidimensional parameter spaces.
Multidimensional parameter pairing is obtained automatically by this algorithm, which can avoid the performance degradation resulting from wrong pairing.
In this section, we present the multidimensional parameter estimation algorithm in polarimetric MIMO radar using PARAFAC quadrilinear decomposition.
In addition, for the ESPRIT-based algorithm, an additional multidimensional parameter pairing for multiple targets is needed.
In order to visualize the multidimensional datasets that result from assays with different mixture compositions, we needed a method that provides two-dimensional interpolated slices through the multidimensional parameter space.
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