| Model | In practice, we never deal with such distributions directly, but rather integrate over them during Gibbs sampling . |
| Model | We achieve these aims by performing Gibbs sampling . |
| Model | Sampling We follow (Neal, 1998) in the derivation of our blocked and collapsed Gibbs sampler . |
| The Model | Following Titov and McDonald (2008) we use a collapsed Gibbs sampling algorithm that was derived for the MG-LDA model based on the Gibbs sampling method proposed for LDA in (Griffiths and Steyvers, 2004). |
| The Model | Gibbs sampling is an example of a Markov Chain Monte Carlo algorithm (Geman and Geman, 1984). |
| The Model | In Gibbs sampling , variables are sequentially sampled from their distributions conditioned on all other variables in the model. |
| Experimental Setup | To improve the model’s convergence rate, we perform two initialization steps for the Gibbs sampler . |
| Experimental Setup | Inference The final point estimate used for testing is an average (for continuous variables) or a mode (for discrete variables) over the last 1,000 Gibbs sampling iterations. |
| Posterior Sampling | We employ Gibbs sampling , previously used in NLP by Finkel et al. |