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Found 6 papers, 2 papers with code
Optimizing Crop Management with Reinforcement Learning and Imitation Learning
Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models
Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation∣state) is nonlinear.
The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model.
Mixture models with a prior on the number of components
A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with Dirichlet weights, and put a prior on the number of components---that is, to use a mixture of finite mixtures (MFM).
Methodology
A simple example of Dirichlet process mixture inconsistency for the number of components
For data assumed to come from a finite mixture with an unknown number of components, it has become common to use Dirichlet process mixtures (DPMs) not only for density estimation, but also for inferences about the number of components.
Inconsistency of Pitman-Yor process mixtures for the number of components
We show that this posterior is not consistent --- that is, on data from a finite mixture, it does not concentrate at the true number of components.
