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.
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.
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.
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.
Another complex step is calculating the weighted mean of the particles, however implementing this step will lead to parallel slowdown due to data dependency.
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.
In this work, we implement this analysis step on graphics processing units (GPUs), an architecture that is optimized for massively parallel computation.
