Numerical results show the behaviour of the method with emphasis on treatment of boundary conditions.
The behaviour of the method for the two extreme cases was specially investigated.
Figure 4 COurarimmediateficate rates.
Such a strategy may have the advantage to partially cancel errors in the activity-concentration estimation, if it is assumed that the behaviour of the method is the same for spheres in a phantom as in for tumours in patients, and could thus be interesting as a way to reduce the uncertainty of the activity concentration estimation.
However, convergence of posterior samples is essential to ensure a good behaviour of the method.
In order to better understand the behaviour of these methods in the PPD, we compare the performance of traditional approaches (LOCF, CC) and theoretically sound techniques (MI, ML), under various MAR mechanisms.
The goal of this simulation exercise is to illustrate the behaviour of these methods in two contrasting scenarios, the first one corresponding to a logistic model with interactions, the second one to a situation with true contrasting profiles.
The behaviour of the two methods will be different when q is large.
The final covariance matrix G is: First, four simulations were carried out to check the behaviour of the different methods for fine mapping.
At the same time, as is always the case when genetic algorithms are used, although simple to implement the behaviour of this method is difficult to understand.
