We selected CRF++ because it implements Equation (1) exactly and its default feature setting is HMM style.
This file contains an R-script that implements Equation 1 for a range of input values of genetic parameters and breeding designs.
The second atom implements Equation 2 to compute the value of the output value in the next state of the system based on the target, the current value of the system and the control value.
Specifically, we implemented Equations 8– 11 in MATLAB using the 'Partial Differential Equation' toolbox.
That is, we implemented equations A81 and A96-121 tourur model.
Both methods implement equations describing the searched ellipse and consequently they can behave differently in the process of fitting.
We implement equations (1) to (33) as a recurrence with a step size parameter Λ to control the number of events using the same p A, p B, p C, p D, p E, δ π and δ τ between successive normalizations, and using Λ δ π and Λ δ τ instead of δ π and δ τ in (25 - 33 25 - 33
However, before discussing such modifications, we first need to describe the two structures implementing Equation 41.
Consequently, we recall the standard structure for implementing Equation 41 in Subsection 3.1 and we introduce an alternative structure in Subsection 3.2; moreover, we compare their complexities in Subsection 3.3.
However, in order to implement Equation 16, we define an instantaneous cost function by replacing the expectation operation with a moving average over a defined window of length ν: J SAM ≈ ∑ l = P + 1 L c ξ ( i, l ) ν 2, with ξ ( i, l ) ≐ ∑ j ∈ I i y ~ j y ~ j - l ∗, (17).
