Mess3

Perfect optimal prediction features (causal states) are usually infinite, making them impractical. Therefore, we need to use approximate features that nearly maintain predictive power. The required number of features and coding cost are then determined by the fractal dimension of the mixed-state simplex. Features obtained by coarse-graining mixed states can be much more efficient than the commonly used length-L Markov models.