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. Recent SBI methods have made use of neural networks (NN) to provide approximate%2C yet expressive constructs for the unavailable likelihood function and the posterior distribution. However%2C the trade-off between accuracy and computational demand leaves much space for improvement. In this work%2C we propose an alternative that provides both approximations to the likelihood and the posterior distribution%2C using structured mixtures of probability distributions. Our approach produces accurate posterior inference when compared to state-of-the-art NN-based SBI methods%2C even for multimodal posteriors%2C while exhibiting a much smaller computational footprint. We illustrate our results on several benchmark models from the SBI literature and on a biological model of the translation kinetics after mRNA transfection.?quality=80&w=800)
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