jaybaxter

7 ms·

1. 31.▲
   by jaybaxter 13y ago

   You are correct that each variable is considered conditionally independent of the other variables given the cluster assignment. CrossCat learns additive models and correlations between continuous variables by using many clusters (the cluste
2. 32.▲
   by jaybaxter 13y ago

   Ah, good question. Currently, it's implemented to only use CrossCat for predictions. However, the great thing about the Bayesian Query Language (BQL, BayesDB's extension of SQL) is that it can be implemented by any joint density e
3. 33.▲
   by jaybaxter 13y ago

   Agreed! CrossCat works really well for many datasets, but I am also very excited about implementing BayesDB with other joint density estimators.
4. 34.▲
   by jaybaxter 13y ago

   Thanks! While the original CrossCat paper focused on binary features, it is in fact much more general. For example, CrossCat uses a beta-bernoulli model for binary features, normal-gamma for continuous, and dirichlet-multinomial for categor
5. 35.▲
   by jaybaxter 13y ago

   This computational cognitive science paper about CrossCat ( http://shaftolab.com/lab_papers/repository/shaftoetal06_cros... ) provides a different perspective that may be more approachable.
6. 36.▲
   by jaybaxter 13y ago

   Hi, I'm one of the lead developers on the project. It's a relatively young project, so keep an eye out for lots of substantial releases over the next few months. So far, we have been focusing on smaller dataset sizes, such as 10,0