A New Approach to Linear Filtering and Prediction Problems

By Tamer Basar. 2001

904 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Optimization & Training

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Citations in the corpus

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review (2018, background): “…and reinforcement learning as a probabilistic inference and learning problem has a long history, going back to original work by Rudolf Kalman (Kalman, 1960), who described how the Kalman smoothing algorithm can also be used to solve control problems with linear dynamics and quadratic costs…”
Stochastic variational inference (2012, background): “…Markov models (and many variants) (Rabiner, 1989; Fine et al., 1998; Fox et al., 2011b; Paisley and Carin, 2009), Kalman ﬁl-ters (and many variants) (Kalman, 1960; Fox et al., 2011a), factorial models (Ghahramani and Jordan, 1997), hierarchical linear regression models (Gelman and Hill, 2007),…”
Deep Kalman Filters (2015, methodology): “Here, we consider a widely used probabilistic model: Kalman ﬁlters (Kalman, 1960).”
Asymmetric Actor Critic for Image-Based Robot Learning (2017, background): “One option is to infer the full state by visual detectors [13], [14] or state prediction ﬁlters [15].”
Dream to Control: Learning Behaviors by Latent Imagination (2019, methodology): “The model mimics a non-linear Kalman ﬁlter (Kalman, 1960), latent state space model, or HMM with real-valued states.”
Covariance Estimation: The GLM and Regularization Perspectives (2011, methodology): “Some of the more explicit uses are in Kalman (1960) for filtering of state-space models and the Gaussian graphical models (Wermuth, 1980).”
Time Series Prediction Using Support Vector Machines: A Survey (2009, background): “This Kalman filtering method was used for both the filtering and smoothing functions.”
A General Method for Robust Bayesian Modeling (2015, background): “, 2003; Blei, 2012), and non-exchangeable data, such as in a hidden Markov model (Rabiner, 1989) or Kalman filter (Kalman, 1960).”
Tourism Demand Modeling: A Time-Varying Parameter Approach (2003, methodology): “Once the SS model is formulated, a convenience algorithm, known as theKalman filter (KF), can be used to estimate the SS model (Kalman 1960).”
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013, methodology): “Another kind of data assimilation scheme is based on the Kalman filter (Kalman 1960) equations, which provides a way to explicitly compute the evolution of the state error covariances.”
Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations (2002, background): “When the process and observation models are linear the familiar equations of the linear Kalman lter provide exact, closed form solutions irrespective of the actual probability distribution of the state estimate [14].”
A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes (2003, methodology): “The ensemble transform Kalman filter is a suboptimal Kalman filter (Kalman 1960; Kalman and Bucy 1961; Daley 1991) with the forecast error covariance matrix estimated by the covariance matrix of the ensemble forecast perturbations.”
Tourism Demand Forecasting: A Time Varying Parameter Error Correction Model (2006, methodology): “The TVP models are normally specified in a state space form (SSF) and estimated by the Kalman filter algorithm (Kalman 1960).”