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Found 6 papers, 2 papers with code
Robust Neural Posterior Estimation and Statistical Model Criticism
In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable black-box parameter inference in simulation models, and consider the implication of a simulation-to-reality gap.
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible.
Approximate Bayesian Computation with Path Signatures
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable.
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.
Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics
The assumptions under which weak convergence results are proved are however restrictive: the target density is typically assumed to be of a product form.
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.
