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Found 5 papers, 2 papers with code
An Extendable Python Implementation of Robust Optimisation Monte Carlo
Our implementation can be used in two ways.
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead.
Likelihood-Free Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations.
Misspecification-robust likelihood-free inference in high dimensions
To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space.
Approximate Bayesian Computation via Population Monte Carlo and Classification
Approximate Bayesian computation (ABC) methods can be used to sample from posterior distributions when the likelihood function is unavailable or intractable, as is often the case in biological systems.
