David Wingate

Publications (3)0 Total impact

• Source

  Available from: psu.edu

  Article: Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

  Matthew Rudary, Satinder Singh, David Wingate
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  ABSTRACT: Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or statespace representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far, PSR models have primarily been developed for domains with discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG) model, a class of PSR models for domains with continuous observations. We show that PLG models subsume Linear Dynamical System models (also called Kalman filter models or state-space models) while using fewer parameters. We also introduce an algorithm to estimate PLG parameters from data, and contrast it with standard Expectation Maximization (EM) algorithms used to estimate Kalman filter parameters. We show that our algorithm is a consistent estimation procedure and present preliminary empirical results suggesting that our algorithm outperforms EM, particularly as the model dimension increases.
  07/2012;
• Source

  Available from: psu.edu

  Article: On Discovery and Learning of Models with Predictive Representations of State for Agents with Continuous Actions and Observations

  David Wingate, Satinder Singh
  System.
• Source

  Available from: psu.edu

  Article: Relational Knowledge with Predictive State Representations

  David Wingate, Vishal Soni, Britton Wolfe, Satinder Singh
  World.