The idea is to allow slowly-varying latent Gaussian state of some dimension (e.g. 3). - [x] Factor out `TreeTrainer` from `TreeCatTrainer`, to make structure learning reusable. - [ ] Implement a `TreeGaussTrainer` (in progress). - [x] Choose sufficient statistics. - [ ] Implement dynamic programming algorithm in `treegauss_add_row()` - [ ] Work out math for `logprob()` and `compute_edge_logits()`. - [ ] Implement data generation for tests. - [ ] Implement `TreeGaussServer`. - [ ] Implement real data import in `format` module.
The idea is to allow slowly-varying latent Gaussian state of some dimension (e.g. 3).
TreeTrainerfromTreeCatTrainer, to make structure learning reusable.TreeGaussTrainer(in progress).treegauss_add_row()logprob()andcompute_edge_logits().TreeGaussServer.formatmodule.