The necessary computational power will strongly depend on the implementation of the algorithms, of course. Parallelization can be readily achieved in the standard algorithm, which can in many places decrease the computation time by a factor virtually proportional to the number of threads. This is true, for example, for solving the Lamm equations, and for the most time-consuming step of building the normal equations matrix. The time for a complete calculation with a full high-resolution grid (10 9 100) for the data shown in Fig. 1 , on a current dual-processor quadcore 3.16-MHz PC (Dell Precision T5400), is only 42 s. 3 The time required for a 10 9 50 grid, which we have already seen leads to an adequate fit within the noise of data acquisition, is 10 s. Finally, it is *15 min for a 20 9 200 grid. In the standard algorithm a Monte Carlo statistical analysis may be desired, for example, in order to examine the statistical accuracy of a particular species population as determined from the integration of the distribution in a certain range. In the standard algorithm, each iteration requires only updating the vector d of the normal Eq. (5) and solving these equations. For the data shown in Fig. 1 , one iteration takes *3 s on a single thread on our PC.