PPM-Decay: A computational model of auditory prediction with memory decay
Fig 11
Illustration of the interpolated smoothing mechanism.
This smoothing mechanism blends together maximum-likelihood n-gram models of different orders. Here the Markov order bound is two, the predictive context is “abracadabra”, and the task is to predict the next symbol. Columns are identified by Markov order; rows are organized into weight distributions, maximum-likelihood distributions, and interpolated distributions. Maximum-likelihood distributions are created by normalizing the corresponding weight distributions. Interpolated distributions are created by recursively combining the current maximum-likelihood distribution with the next-lowest-order interpolated distribution. The labelled arrows give the weight of each distribution, as computed using escape method “A”. The “Order = −1” column identifies the termination of the interpolated smoothing, and does not literally mean a Markov order of −1.