no code implementations • 20 Sep 2022 • Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew T. Harrison, Carla Ferreira, Zahra Kalantari, Naira Hovakimyan
Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.
1 code implementation • 1 May 2020 • Michael C. Burkhart, David M. Brandman, Brian Franco, Leigh R. Hochberg, Matthew T. Harrison
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.
no code implementations • 23 Aug 2016 • Michael C. Burkhart, David M. Brandman, Carlos E. Vargas-Irwin, Matthew T. Harrison
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model.
1 code implementation • 22 Feb 2015 • Jeffrey W. Miller, Matthew T. Harrison
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
no code implementations • NeurIPS 2013 • Jeffrey W. Miller, Matthew T. Harrison
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.
no code implementations • 30 Aug 2013 • Jeffrey W. Miller, Matthew T. Harrison
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.