Abstract
This paper describes a multi-scale stochastic model defined on a multiwavelet structure. Previously such stochastic models are based either on a single or multiple binary tree as well as on an ordinary wavelet structure. The proposed model lies on a tree structure consists of several sets of data coefficients as a result of multiwavelet transformation. Each data sets is linked only through initial data set at root node and conditionally independent given this initial state. Multiwavelet possesses several interesting properties like simultaneously short support, symmetry and orthogonality. The effect of these properties on the proposed model is shown through simulation of smoothing process of a certain fractal signal. It will be demonstrated that several improvements over previously announced results are obtained.
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