Process Simulation Using Randomized Markov Chain and Truncated Marginal Distribution

Generating pseudo random objects is one of the key issues in computer simulation of complex systems. Most earlier simulation systems include procedures for the generation of independent and identically distributed random variables or some classical random processes, such as white noise. In this paper we propose a new approach to the generation of wide ranges of processes that are characterized by marginal distribution and autocorrelation function that are significant in many cases. The proposed algorithm is based on the use of truncated distribution that gives more simplicity and efficiency in comparison with the previous one. The effectiveness of the proposed algorithm is verified using computer simulation of various real examples.

[1]  O. J. Dahl,et al.  Simula Begin , 1979 .

[2]  J. E. Gentle,et al.  Principles of Random Variate Generation. , 1990 .

[3]  D. Blacknell.  New method for the simulation of correlated K-distributed clutter , 1994 .

[4]  R. Matzner,et al.  On the construction of a random process with given power spectrum and probability density function , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

[5]  J. Dagpunar.  Principles of Random Variate Generation , 1988 .

[6]  G. Enderlein.  Wilks, S. S.: Mathematical Statistics. J. Wiley and Sons, New York–London 1962; 644 S., 98 s , 1964 .

[7]  David R. Cox,et al.  The statistical analysis of series of events , 1966 .

[8]  Hugh Griffiths,et al.  Modelling Spatially Correlated K-Distributed Clutter , 1991 .

[9]  Erwin Kreyszig,et al.  Introductory Mathematical Statistics. , 1970 .

[10]  Singiresu S. Rao,et al.  Optimization Theory and Applications , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Thomas J. Schriber,et al.  Simulation Using GPSS , 1974 .

[12]  Oscar H. Bustos,et al.  Simulation of correlated intensity SAR images , 1999, XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481).

[13]  Alejandro C. Frery,et al.  A MONTE-CARLO STUDY OF CLASSICAL SPECTRAL ESTIMATION OF THE BACKSCATTER FROM CORRELATED K-DISTRIBUTED SPECKLED SAR IMAGES , 1999 .

[14]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[15]  Gerd Ronning.  A Simple Scheme for Generating Multivariate Gamma Distributions with Non-Negative Covariance Matrix , 1977 .

[16]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[17]  William G. Bulgren.  Discrete system simulation , 1982 .

[18]  J. Hammersley,et al.  Monte Carlo Methods , 1965 .