An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Juho Timonen,
Nikolas Siccha,
Ben Bales
et al.
Abstract:Statistical models can involve implicitly defined quantities, such as solutions to nonlinear ordinary differential equations (ODEs), that unavoidably need to be numerically approximated in order to evaluate the model. The approximation error inherently biases statistical inference results, but the amount of this bias is generally unknown and often ignored in Bayesian parameter inference. We propose a computationally efficient method for verifying the reliability of posterior inference for such models, when the… Show more
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