Let Ω m be the parameter space of model (mathcal {M}_{m}), m=1,…,9.
Let P n = { ( T, z ) : T = [ a − d, …, a d ] ∈ R 2 d + 1, z ∈ R } be the parameter space of the classical CNNs, where n = 2 d + 2. Theorem 2.8 indicates that the P n can be partitioned into a finite number of subregions such that each subregion has the same mosaic patterns.
The dashed curve is the parameter space below which the entire droplet is hypercooled.
(a) is a synthesized edge image of three straight lines; (b) is the parameter space of the Hough transform.
For each group of results, (a) is the time-frequency image of scattering centers, and (b) is the parameter space obtained through the modulo GHT method.
The right side of Figure 10 is the parameter space where ρ ∈[−100, 100], and the left side shows the results of our improved Hough transform at different coordinate origins.
Example 5.7 Suppose that U = { h 1, h 2, h 3, h 4, h 5, h 6 } is the universe and E = { e 1, e 2, e 3, e 4, e 5 } is the parameter space.
Consider an estimation of (g(theta)) based on a sample (x_{1},ldots,x_{n},x_{n}) from some member of a family of distribution functions (F_{theta}(x)), (thetainOmega), where Ω is the parameter space.
Of particular interest is the parameter space for precipitation and maximum temperature.
Whereas usually Δ n−1 is the parameter space of the multinomial distribution, we have to work with quasispecies space Q n − 1 as only elements from this set can represent true underlying quasispecies distributions.
To compare two models M i and M j, given experimental data and known measurement noise S, we compute the Bayes factor (Kass and Raftery, 1995): (5) where and are the parameter spaces for models M i and M j, respectively.
