C_opts = set or list of equations

Options passed to C.  The available options depend on C; see GetCompiledProc for details. The default is an empty set.

include_nominal = true or false

True means include, as the first data record, the result of using the nominal values of the random variables. Depending on use_mean, the nominal value of a parameter is either retrieved from the model or computed from the random variable using Statistics[Mean]. The default is false.

num_sims = positive integer

The number of simulations. Does not include the simulation using nominal values; see include_nominal. The default is 10.

ret_record = true or false

True means return a record with the following fields:

data: a list of Matrices, these are the simulation results;

params : a list of the parameter names;

values : a Matrix of the parameter values. The k-th column contains the values for the k-th name in the params field.

errors : a list of strings of the errors that occurred during the corresponding simulation. This field is present only if the C module was generated with the witherror option to GetCompiledProc.

False means return a list of records, one for each simulation. Each record has the following fields:

data: the Matrix that is the simulation result; the first column is the time samples, the remaining columns are the values of the outputs of C.

params: the set of equations specifying the value of each parameter.

errors: a string corresponding to an error that occurred during the simulation; the empty string indicates no error occurred. This field is present only if the C module was generated with the witherror option to GetCompiledProc.

The default is false.

use_mean = true or false

True means use Statistics[Mean] to compute the nominal value of the parameters, otherwise use the values assigned by the model. This option is used with include_nominal. The default is false.

use_threads = true or false

True means use the Threads package to compute the results in parallel. The default is true.

The module returned by GetCompiledProc has a SetObjective export that uses a provided procedure to manipulate the output. Here we pass it a procedure to square the output (column two of the matrix).

C1:-SetObjective( proc(M)
                  local i;
                      for i to upperbound(M,1) do
                          M[i,2] := M[i,2]^2;
                      end do;
                      M;
                  end proc):

Run a MonteCarlo analysis with the squared output.

$\mathrm{results2}≔\mathrm{MonteCarlo}\left(\mathrm{C1}\,\left\{C=\mathrm{rv}\left(5\,0.1\right)\,L=\mathrm{rv}\left(4\,0.1\right)\right\}\,\mathrm{C\_opts}=\left\{\mathrm{ds}=0.01\,\mathrm{tf}=1\right\}\,\mathrm{include\_nominal}\right)\:$

plot({seq(rec:-data[..,[1,2]], rec=results2)});

The objective procedure is not limited to modifying the matrix of simulation data, it can also compute a value from the matrix and return that. Here we assign a procedure that returns the peak of the squared result.

C1:-SetObjective(proc(M) max(map(x->x^2, M[..,2])) end proc):

Run a MonteCarlo analysis; use the ret_record option to return a record.

$\mathrm{results3}≔\mathrm{MonteCarlo}\left(\mathrm{C1}\,\left\{C=\mathrm{rv}\left(5\,0.1\right)\,L=\mathrm{rv}\left(4\,0.1\right)\right\}\,\mathrm{C\_opts}=\left\{\mathrm{ds}=0.01\,\mathrm{tf}=1\right\}\,\mathrm{include\_nominal}\,\mathrm{ret\_record}\right)\:$

Compute the range over which the peak value varied.

$\mathrm{evalf}\left[2\right]\left(\left(\min ..\max \right)\left(\mathrm{results3}:-\mathrm{data}\right)\right)$

$0.0031..0.0038$