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Frontline Systems, Inc. |
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The Solver Platform SDK includes complete facilities for creating models in C++, C#, VB.NET, Visual Basic, Java, and MATLAB with uncertain variables and functions, running Monte Carlo simulations, and collecting statistics from the Monte Carlo samples. You could pay $1,200 or more for other software libraries that provide only probability distribution modeling and Monte Carlo simulation -- but in the Solver Platform SDK, all this power is included at no extra cost.
Sampling MethodsThe Solver Platform SDK includes four different, high quality random number generators, covering the full spectrum of tradeoffs between long periods and statistical independence of the samples:
Monte Carlo samples are generated from a wide range of probability distributions, using any of three methods:
Sobol numbers are an innovation in the Solver Platform SDK that's not found in other software for Monte Carlo simulation. They are widely used by application developers in quantitative finance. For low to moderate dimensional problems, Sobol numbers offer the "best of both worlds" -- the speed of Standard Monte Carlo with the "coverage" of Latin Hypercube sampling. Probability DistributionsThe Solver Platform SDK provides a complete set of analytic probability distributions. And you can specify shifting and truncation to customize your probability distributions.
You can easily create an instance of a Distribution object, with the properties of any of these probability distributions. By simply accessing properties of this object, you can obtain the probability density (PDF) or cumulative density (CDF) function, or analytic values for the moments of the distribution, based on its type and parameters. Distribution FittingThe Solver Platform SDK makes it easy to fit an
analytic distribution and its parameters to sample data. You
can specify the distribution type and ask the SDK to find the
best-fitting parameters, or you can just supply the sample data,
specify continuous or discrete, and let the SDK automatically choose
the best-fitting distribution type and the best parameters. This is
illustrated in the Example Source Code.
The SDK can fit 22 different distributions (from the list of 38
above), including both continuous and discrete distributions. The Solver Platform SDK makes it easy to create
correlated input distributions, by creating a DoubleMatrix object
that specifies rank correlations between two or more
distributions. You simply assign this correlation matrix to
the appropriate property of the Model object. This is
illustrated in the Example Source Code. To consistently specify correlations among multiple
distributions, a rank correlation matrix must be positive
semidefinite (PSD). Users often have "desired
correlations" among the key distributions, but insufficient information
to fill out the matrix. The Solver Platform SDK includes
methods to test a DoubleMatrix for positive semidefiniteness (IsPSD)
and to transform a non-PSD matrix into a "nearest" matrix
that is positive semidefinite (MakePSD). Unlike other
software, the SDK can find a PSD matrix that leaves your "desired
correlations" among key distributions nearly unchanged. Statistical ResultsIn the Solver Platform SDK, you can obtain any of the statistics listed below for both uncertain variables and uncertain functions, by simply accessing the appropriate property or method of a Statistics object embedded in each Variable and Function object. You can obtain confidence intervals (CI) for the mean or standard deviation, or the number of Monte Carlo trials required to obtain a result within your specified confidence interval.
100 percentile values (0 to 99) are computed for each variable and function. In addition, you can obtain the observed correlation in the Monte Carlo sample between any two uncertain functions, or between an uncertain function and an uncertain variable, by accessing a property of the Function object. Risk MeasuresThe Solver Platform SDK goes beyond computation of "standard moments" to compute several risk measures popular in quantitative finance applications. Again you can obtain these values by simply accessing the appropriate property or method of a Statistics object embedded in each Variable and Function object.
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