Corpus ID: 211252350

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems

@article{Kersting2020DifferentiableLF,
  title={Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems},
  author={Hans Kersting and Nicholas Kr{\"a}mer and Martin Schiegg and Christian Daniel and Michael Tiemann and Philipp Hennig},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.09301}
}
  • Hans Kersting, Nicholas Krämer, +3 authors Philipp Hennig
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Likelihood-free (a.k.a. simulation-based) inference problems are inverse problems with expensive, or intractable, forward models. ODE inverse problems are commonly treated as likelihood-free, as their forward map has to be numerically approximated by an ODE solver. This, however, is not a fundamental constraint but just a lack of functionality in classic ODE solvers, which do not return a likelihood but a point estimate. To address this shortcoming, we employ Gaussian ODE filtering (a… CONTINUE READING

    Citations

    Publications citing this paper.
    SHOWING 1-3 OF 3 CITATIONS

    A Fourier State Space Model for Bayesian ODE Filters

    VIEW 4 EXCERPTS
    CITES METHODS

    Bayesian ODE Solvers: The Maximum A Posteriori Estimate

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Variational Autoencoding of PDE Inverse Problems

    VIEW 1 EXCERPT
    CITES METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 54 REFERENCES

    Scalable Variational Inference for Dynamical Systems

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    A Conceptual Introduction to Hamiltonian Monte Carlo

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL

    Bayesian Filtering and Smoothing

    • Simo Särkkä
    • Computer Science
    • Institute of Mathematical Statistics textbooks
    • 2013
    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Information Theory, Inference, and Learning Algorithms

    VIEW 1 EXCERPT
    HIGHLY INFLUENTIAL

    Optimization Methods for Large-Scale Machine Learning

    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    The frontier of simulation-based inference

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL