Inference for dynamical systems: a Bayesian perspective

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  Inference for dynamical systems: a Bayesian perspective
  Oksana Chkrebtii

  An important test of a mathematical model is how well it describes a given phenomenon, which is often observed only indirectly and with error.  This talk will provide an introduction to the statistical problem of inference for nonlinear dynamical systems. We will take a Bayesian perspective, constructing hierarchical models that incorporate prior knowledge and impose required constraints on model components.