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Robust and integrative Bayesian neural networks for likelihood-free parameter inference
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation
The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator.
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