Last updated: 2019-07-17
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Knit directory: lieb/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8b30694 | hillarykoch | 2019-07-17 | update about page |
| html | 8b30694 | hillarykoch | 2019-07-17 | update about page |
| html | e36b267 | hillarykoch | 2019-07-17 | update about page |
| html | a991668 | hillarykoch | 2019-07-17 | update about page |
| html | a36d893 | hillarykoch | 2019-07-17 | update about page |
| html | e8e54b7 | hillarykoch | 2019-07-17 | update about page |
| html | f47c013 | hillarykoch | 2019-07-17 | update about page |
| Rmd | 50cf23e | hillarykoch | 2019-07-17 | make skeleton |
| html | 50cf23e | hillarykoch | 2019-07-17 | make skeleton |
We are now just about ready to set up our MCMC. First, we need to determine the hyperparameters in the priors of our Gaussian mixture. These are all calculated in an empirical Bayesian manner – that is, we can recycle information from the pairwise fits to inform our priors in the full-information mixture. This task can be split into 2 sub-tasks:
computing the prior hyperparameters for the cluster mixing weights
computing every other hyperparameter
The former is the most essential, as it helps us remove more candidate latent classes, ensuring that the number of clusters is fewer than the number of observations. An important note: this is the only step of LIEB that requires some sort of human intervention, but it does need to happen. A threshold, called δ in the manuscript, determines how strict one is about including classes in the final model. δ∈{0,1,…,ˆM}, where ˆM is the number of candidate latent classes determined by the get_reduced_classes() function, as calculated in the previous step.