The Smolyak or sparse quadrature is proposed, as well as the regression approach with experimental design, to approximate the expansion coefficients dealing with high dimensional cost.
One approach combines cost and effect data on a two dimensional cost effectiveness plane (for example, Figure 1) [ 5- 7].
These lattices are processed using the lattice indexing technique described in [103] so that the lattices of all the utterances in the search collection are converted from the individual WFSTs to a single generalized factor transducer structure in which the start-time, the end-time, and the lattice posterior probability of each word token are stored as three-dimensional costs.
The procedure to calculate value of information is demonstrated using a one-dimensional cost function.
Figure 5 shows the three-dimensional cost space in horizontal x and vertical y of the image versus disparity d search range.
A one-dimensional cost aggregation is applied to the vertical direction at first and the intermediate result is aggregated again in horizontal direction.
It is also noted that optimization of the non-dimensional cost function does not translate into optimization of the thermal efficiency.
The lattice indexing technique, described in [124], first converts the word lattices of all the utterances in the speech data from individual WFSTs to a single generalized factor transducer structure that stores the start-time, end-time, and the lattice posterior probability of each word token as a three-dimensional cost.
The lattice indexing technique, described in [66], first converts the word lattices of all the utterances from individual weighted finite state transducers (WFST) to a single generalized factor transducer structure that stores the start-time, end-time, and the lattice posterior probability of each word token as a 3-dimensional cost.
A two-dimensional cost-effectiveness plane will show the uncertainty in costs and effects through a cost-effectiveness ellipse.
Dispersal Vicariance Analysis is based on the dispersal vicariance approach (Ronquist 1997), which consists of the optimization of a three-dimensional cost matrix derived from a simple biogeographic model.
