Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models

Preprint, Other literature type English OPEN
Bhaumik, Prithwish; Ghosal, Subhashis;
(2017)
  • Publisher: Bernoulli Society for Mathematical Statistics and Probability
  • Journal: issn: 1350-7265
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.3150/16-BEJ856
  • Subject: Mathematics - Statistics Theory | Runge–Kutta method | approximate likelihood | spline smoothing | ordinary differential equation | Bernstein–von Mises theorem | Bayesian inference | 62J02, 62G08, 62G20, 62F12, 62F15
    arxiv: Statistics::Computation | Statistics::Methodology

Often the regression function is specified by a system of ordinary differential equations (ODEs) involving some unknown parameters. Typically analytical solution of the ODEs is not available, and hence likelihood evaluation at many parameter values by numerical solution... View more
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