ABSTRACT
The non-Gaussian Karhunen-Loève (K-L) expansion has been used to generate a non-Gaussian process using an iterative scheme. Numerical results show that different non-Gaussian processes can be generated satisfying the same prescribed covariance function and marginal distribution by changing the assumed starting distribution of the K-L random variables. Non-Gaussian K-L processes produced by assuming an initial Gaussian distribution for the K-L random variables appear to be translation processes. When the K-L random variables were assigned a lognormal distribution before the iteration procedure, the resulting process is clearly non-translation. Hence, it would appear that translation processes form a subset of K-L processes. In other words, the class of non-Gaussian K-L processes is larger and potentially capable of providing better fit to observed data.