Web: http://arxiv.org/abs/2203.07961

Sept. 23, 2022, 1:12 a.m. | Hippolyte Verdier, François Laurent, Christian Vestergaard, Jean-Baptiste Masson, Alhassan Cassé

cs.LG updates on arXiv.org arxiv.org

We introduce a simulation-based, amortised Bayesian inference scheme to infer
the parameters of random walks. Our approach learns the posterior distribution
of the walks' parameters with a likelihood-free method. In the first step a
graph neural network is trained on simulated data to learn optimized
low-dimensional summary statistics of the random walk. In the second step an
invertible neural network generates the posterior distribution of the
parameters from the learnt summary statistics using variational inference. We
apply our method to …

arxiv complexity computational inference linear

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