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Found 2 papers, 1 papers with code
Deep Signature Statistics for Likelihood-free Time-series Models
Our approach leverages deep signature transforms, trained concurrently with a neural density estimator, to produce informative statistics for multivariate sequential data that encode important geometric properties of the underlying path.
Generalized Posteriors in Approximate Bayesian Computation
Approximate Bayesian computation (ABC) has emerged as a key method in simulation-based inference, wherein the true model likelihood and posterior are approximated using samples from the simulator.
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