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Bayesian parameter inference is one of the key elements for model selectionin cosmological research. However, the available inference tools require alarge number of calls to simulation codes which can lead to high and sometimeseven infeasible computational costs. In this work we propose a new way ofemulating simulation codes for Bayesian parameter inference. In particular,this novel approach emphasizes the uncertainty-awareness of the emulator, whichallows to state the emulation accuracy and ensures reliable performance. With afocus on data efficiency, we implement an active learning algorithm based on acombination of Gaussian Processes and Principal Component Analysis. We findthat for an MCMC analysis of Planck and BAO data on the $\Lambda$CDM model (6model and 21 nuisance parameters) we can reduce the number of simulation callsby a factor of $\sim$500 and save about $96\%$ of the computational costs.

Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA