Latin hypercube sampling followed optimization

The search space can be modelled using the Montecarlo technique, but also Latin Hypercube sampling. This method solves sampling design as a single-objective optimization problem using dedicated combinatorial local search operators. points that must follow a given statistical distribution. 2) Screening: in the simulationвЂs pilot phase the really important inputs can be identified through a novel technique, called sequential bifurcation, which uses aggregation and sequential experimentation. 2.1 Conditioned Latin Hypercube Sampling Minasny and McBratney 18 devised a method for obtaining op-timal sampling design in the presence of ancillary data, namely cLHS. 1) Validation, in which the availability of data on the real system determines which type of statistical technique to use for validation.

be forward sampling, we need to make sure that the columns of the LHS matrix follow the. The main questions are: when should which type of analysis be applied which statistical techniques may then be used? This paper claims that the proper sequence to follow in the evaluation of simulation models is as follows. Figure 4: A LHSs,loo matrix for m 3 variables and sample size n 100.

This paper reviews five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. Latin Hypercube sampling (LHS) is inspired by the Latin square experimental design, and is designed to ensure that each value of a variable is represented regardless of its resultant importance.

Latin hypercube sampling followed optimization