Another complex step is calculating the weighted mean of the particles, however implementing this step will lead to parallel slowdown due to data dependency.
Therefore, this paper proposed a GeoHash algorithm to implement this step: through a certain rule [12, 13], a string to represent the latitude and two longitude coordinates.
Note that, R B, β 0, and M 1 are all range-dependent parameters, we have to implement this step in each range gate.
We used the STATA 9.0 software and the procedure "sttocc," which takes into account survival times, to implement this step.
While implementing this step, we first mark all the nodes that are ancestral to the modified node and then update only the MA vectors of the marked nodes.
3. Calculate an estimate of the parameters θ X that maximizes the conditional prior density function by, arg max θ X ∏ X i j, W i j = 1 p (X i j | X - { X i j }, θ X ) We also implement this step using Differential Evolution.
2. Estimate parameters θ Y that maximizes the data likelihood function given by, arg max θ Y ∏ X i j, W i j = 1 p (Y A i, Y B i, Y A j, Y B j | X i j, X i j *, θ Y ) We implement this step using Differential Evolution, which is similar to the genetic algorithm.
Bringing additional people into the decision-making can be beneficial in tightening family bonds or it can be a nightmare, so implement this step at your own discretion.
As this methodology is employing an agent module, the agent is also implemented in this step.
