Matlab latin hypercube sampling lognormal

#Matlab latin hypercube sampling lognormal generator
#Matlab latin hypercube sampling lognormal full
#Matlab latin hypercube sampling lognormal software

To consistently specify correlations among multiple distributions, a rank correlation matrix must be positive semidefinite (PSD). This is illustrated in the Example Source Code. You simply assign this correlation matrix to the appropriate property of the Model object. The Solver SDK Platform makes it easy to create correlated input distributions, by creating a DoubleMatrix object that specifies rank correlations between two or more distributions. The SDK can fit 22 different distributions (from the list of 38 above), including both continuous and discrete distributions. 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. The Solver SDK Platform makes it easy to fit an analytic distribution and its parameters to sample data. 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. You can easily create an instance of a Distribution object, with the properties of any of these probability distributions. And you can specify shifting and truncation to customize your probability distributions. The Solver SDK Platform provides a complete set of analytic probability distributions. 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. They are widely used by application developers in quantitative finance.

#Matlab latin hypercube sampling lognormal software

Sobol numbers are an innovation in the Solver SDK Platform that's not found in other software for Monte Carlo simulation. Monte Carlo samples are generated from a wide range of probability distributions, using any of three methods:

#Matlab latin hypercube sampling lognormal generator

This generator has the longest period of 2 19937-1, but the samples are not as 'equidistributed' as for the WELL1024 and CMRG generators.

Mersenne Twister generator of Matsumoto and Nishimura.

This very new generator combines a long period of 2 1024 with very good statistical independence.

Well Equidistributed Long-period Linear (WELL1024) generator of Panneton, L'Ecuyer and Matsumoto.

This generator has a period of 2 191, and excellent statistical independence of samples within its period.

Combined Multiple Recursive Generator (CMRG) of L'Ecuyer.

This generator has a period of 2 31-2, and very good statistical independence of samples.

Park-Miller 'Minimal' Generator with Bayes-Durham shuffle and safeguards.

#Matlab latin hypercube sampling lognormal full

The Solver SDK Platform includes four different, high quality random number generators, covering the full spectrum of tradeoffs between long periods and statistical independence of the 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 SDK Platform, all this power is included at no extra cost. The Solver SDK Platform 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.