Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Abstract Recently, a. framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. In this paper, we show that this model class is also quite rich. In particular, we describe how 1-D Markov processes and 2-D Ma.rkov random fields (MRF's) can be represented within this framework. Markov processes in one-dimension and Markov randoml fields in two-dimensions are widely used classes of models for analysis, design and statistical inference. The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2-D MIR,F's are well known to be very difficult to analyze due to their non-causal structure, and thus their use typically leads to colmputationally intensive algoritlllhms for smoothing and parameter idlentification. Our multiscale representations are based on scale-recursive models, thus providing a framework for the development of new and efficient algorithms for Markov processes and MRF's. In 1-D, the representation generalizes the mid-point deflection construction of Brownian motion. In 2-D, we use a further generalization to a. "mid-line" deflection construction. Our exact representations of 2-D MRF's are of potentially high dimension, and this motivates a class of approximate models ba.sed on one-dim7ensional wavelet transforms. We demonstrate the use of these models in the context of texture representation and in particular, we show how they can be used as approximations for or alternatives to well-known MRF texture models: We illustrate how the quality of the representations varies a.s a. function of the underlying MRF and the complexity of the wavelet-based approximate representation.