# Efficient updating of kriging estimates and variances

### Efficient updating of kriging estimates and variances

We need a proper knowledge of both the techniques of simulation modeling and the simulated systems themselves.The scenario described above is but one situation where computer simulation can be effectively used.

For example, in a consumer retail environment it can be used to find out how the roles of consumers and employees can be simulated to achieve peak performance.Simulation early in the design cycle is important because the cost to repair mistakes increases dramatically the later in the product life cycle that the error is detected.Another important application of simulation is in developing "virtual environments" , e.g., for training.A simulation is the execution of a model, represented by a computer program that gives information about the system being investigated.The simulation approach of analyzing a model is opposed to the analytical approach, where the method of analyzing the system is purely theoretical.As this approach is more reliable, the simulation approach gives more flexibility and convenience.

The activities of the model consist of events, which are activated at certain points in time and in this way affect the overall state of the system.

For practical purposes, the main idea of the central limit theorem (CLT) is that the average of a sample of observations drawn from some population with any shape-distribution is approximately distributed as a normal distribution if certain conditions are met.

In theoretical statistics there are several versions of the central limit theorem depending on how these conditions are specified.

In addition to its use as a tool to better understand and optimize performance and/or reliability of systems, simulation is also extensively used to verify the correctness of designs.

Most if not all digital integrated circuits manufactured today are first extensively simulated before they are manufactured to identify and correct design errors.

In applications of the central limit theorem to practical problems in statistical inference, however, statisticians are more interested in how closely the approximate distribution of the sample mean follows a normal distribution for finite sample sizes, than the limiting distribution itself.