| Experiments | Since our implementation is based on Unicode and learns all hyperparameters from the data, we also confirmed that NPYLM segments the Arabic Gigawords equally well. |
| Inference | 9 Sample hyperparameters of 9 |
| Inference | ba Na) to estimate A from the data for given language and word type.7 Here, l‘(:c) is a Gamma function and a, b are the hyperparameters chosen to give a nearly uniform prior distribution.8 |
| Pitman-Yor process and n-gram models | 6, d are hyperparameters that can be learned as Gamma and Beta posteriors, respectively, given the data. |
| Hierarchical Topic Models 3.1 Latent Dirichlet Allocation | (1) where 04 and 77 are hyperparameters smoothing the per-attribute set distribution over concepts and per-concept attribute distribution respectively (see Figure 2 for the graphical model). |
| Hierarchical Topic Models 3.1 Latent Dirichlet Allocation | The hyperparameter *y controls the probability of branching via the per-node Dirichlet Process, and L is the fixed tree depth. |
| Hierarchical Topic Models 3.1 Latent Dirichlet Allocation | Hyperparameters were a=0.1, 7720.1, 721.0. |