Thursday, November 28, 2019

Thursday, November 28, 2019 — 4:00 PM EST

Bayesian inference of dynamic systems via constrained Gaussian processes

Ordinary differential equations are an important tool for modeling behaviors in science, such as gene regulation, epidemics, etc.  An important statistical problem is to infer and characterize the uncertainty of parameters that govern the equations.  We present a fast Bayesian inference method using constrained Gaussian processes, such that the derivatives of the Gaussian process must satisfy the dynamics of the differential equations.  Our method completely avoids the numerical solver and is thus practically fast to compute. Our construction is cleanly embedded in a rigorous Bayesian framework, and is demonstrated to yield fast and reliable inference in a variety of practical scenarios.

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