This paper describes a general method for synthesizing discrete 2D and 3D output by building probabilistic models of rasterized or voxelized training data, and subsequently synthesizing new data iteratively by substituting cells or groups of cells in accordance with a learned transition matrix. The process is non-deterministic, stochastic and unsupervised. The size of the source data and output is arbitrary, and the source and output data can have an arbitrary set of cell states. Possible variations of the process are discussed, as well as possible applications in design processes on multiple scales.