English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Likelihood-free Bayesian inference algorithms are popular methods forcalibrating the parameters of complex, stochastic models, required when thelikelihood of the observed data is intractable. These algorithmscharacteristically rely heavily on repeated model simulations. However,whenever the computational cost of simulation is even moderately expensive, thesignificant burden incurred by likelihood-free algorithms leaves them unviablein many practical applications. The multifidelity approach has been introduced(originally in the context of approximate Bayesian computation) to reduce thesimulation burden of likelihood-free inference without loss of accuracy, byusing the information provided by simulating computationally cheap, approximatemodels in place of the model of interest. The first contribution of this workis to demonstrate that multifidelity techniques can be applied in the generallikelihood-free Bayesian inference setting. Analytical results on the optimalallocation of computational resources to simulations at different levels offidelity are derived, and subsequently implemented practically. We provide anadaptive multifidelity likelihood-free inference algorithm that learns therelationships between models at different fidelities and adapts resourceallocation accordingly, and demonstrate that this algorithm produces posteriorestimates with near-optimal efficiency.

Efficient Multifidelity Likelihood-Free Bayesian Inference with Adaptive Computational Resource Allocation