Mixture Kalman ®lters

By Rong Chen, Jun S. Liu. 2000

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. Paul Fearnhead
2. Thomas Bo Schön
3. Daniel Lundén
4. Jan Kudlicka
5. David Broman
6. Z. Liu
7. Joschka Boedecker
8. Lawrence M. Murray

Citations in the corpus

• Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images (2015, methodology): “In a broader context our model is also related to work on learning non-linear (latent) dynamic models for observations in a Markov decision process through, e.g., hidden Markov models and Kalman filters[34, 35, 36].”
• A family of algorithms for approximate Bayesian inference (2001, background): “One is sequential Monte Carlo, a family of nondeterministic techniques (Liu & Chen, 2000; Carpenter et al., 1999).”
• Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs (2017, background): “The Rao–Blackwellized particle filter, also known as the marginalized particle filter, was designed to achieve precisely this [2, 22].”
• On‐line inference for multiple changepoint problems (2007, background): “A similar problem of increasing computational cost occurs in the analysis of some hidden Markov models – though generally computational cost increases exponentially with time (Chen and Liu, 2000).”