Corpus ID: 216078743

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

@article{Meeds2015OptimizationMC,
  title={Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference},
  author={Edward Meeds and M. Welling},
  journal={ArXiv},
  year={2015},
  volume={abs/1506.03693}
}
  • Edward Meeds, M. Welling
  • Published 2015
  • Computer Science, Mathematics
  • ArXiv
  • We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is deterministic. For each instantiation of u we run an optimization procedure to minimize the distance between summary statistics of the simulator and the data. After reweighing these samples… CONTINUE READING
    6 Citations
    A Likelihood-Free Reverse Sampler of the Posterior Distribution
    • 11
    • PDF
    The ABC of Simulation Estimation with Auxiliary Statistics
    • 28
    • PDF
    Asymptotically exact inference in differentiable generative models
    • 18
    • PDF

    References

    SHOWING 1-10 OF 23 REFERENCES
    Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
    • 151
    • PDF
    A Likelihood-Free Reverse Sampler of the Posterior Distribution
    • 11
    • PDF
    Hamiltonian ABC
    • 29
    • PDF
    Sequential Monte Carlo samplers
    • 1,268
    • Highly Influential
    • PDF
    Approximate Bayesian computation using indirect inference
    • 80
    • PDF
    Efficient likelihood-free Bayesian Computation for household epidemics
    • P. Neal
    • Computer Science
    • Stat. Comput.
    • 2012
    • 27
    • PDF
    Asynchronous Anytime Sequential Monte Carlo
    • 44
    • PDF
    Non-linear regression models for Approximate Bayesian Computation
    • 398
    • PDF