In order to permit the incorporation of any a priori information about parameters and any distribution of data (not only of gaussian type) we propose to formulate the problem not using single quantities (such as bounds, means, etc) but using probability density functions for data and parameters...
places, and organizations, leading to the “named entity” categorization. In more recent years within the biomedical domain, the type of semantic class has broadened very widely, to include things like biological processes ormolecular functionsthat are...
After combining it with the original amplitude spectrum, an inverse Fourier transformation was performed. For each face image presentation (e.g., for each repetition of the image of Ari), a new random phase structure was applied, i.e., all presented face images had a unique noise pattern....
4.0. Special Functions Julia: JuliaMath/SpecialFunctions.jl: Special mathematical functions in Julia InverseFunction JuliaMath/InverseFunctions.jl: Interface for function inversion in Julia JuliaStats/StatsFuns.jl: Mathematical functions related to statistics. JuliaStats/LogExpFunctions.jl: Julia package fo...
There are some types of update functions for solving the prior minimization problem iteratively. In this study, we used the “multiplicative update rules.” The initial entries for\(W\)and\(H\)were drawn from a uniform distribution. The number of iterations of the update function was 500. Th...
Section 3 introduces the model setup and derives the inverse demand functions. Section 4 analyzes the benchmark cases LM and DM. Section 5 investigates the case GM and obtains the retailer’s optimal information sharing strategy. Section 6 discusses the extension that includes consumers’ valuation ...
Then the functions (6.13)p→Inm(AX×BY,PXY) on [0,1] are assumed to be bounded from below. Now we are ready to present the main result of Forte and Gupta (1985) (in a slightly different but equivalent form). THEOREM 6.3 The measures of uncertainty (Inm) satisfy the properties (Al...
inverse of the metric tensor. Mirror descent induces non-Euclidean structure by solving iterative optimization problems using different proximity functions. It was proven that mirror descent induced by a Bregman divergence proximity functions is equivalent to the natural gradient descent algorithm on the ...
functions (Farthing, 1992). Once objects or instances are classified, thisperceptual informationis analysed and interpreted according to appropriate schemata (Fiske & Taylor, 1991). Schema is defined as a cognitive structure that comprise knowledge about the attributes of a category and the links ...
We show how information limitation leads to sigmoid interaction rate functions that can stabilize antagonistic interactions and destabilize mutualistic ones; as a species or type becomes rare, information on its whereabouts also becomes rare, weakening coupling with consumers, pathogens and mutualists. ...