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Found 8 papers, 2 papers with code
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC
We present a framework for approximate Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained due to computational constraints, which is becoming increasingly common for applications of complex models.
Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation
Surrogate models can be used to accelerate approximate Bayesian computation (ABC).
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
We propose a numerical method to fully quantify the uncertainty in, for example, ABC posterior moments.
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained.
A Bayesian model of acquisition and clearance of bacterial colonization
Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations.
ELFI: Engine for Likelihood-Free Inference
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
Efficient acquisition rules for model-based approximate Bayesian computation
We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty.
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible.
