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.%20Recent%20SBI%20methods%20have%20made%20use%20of%20neural%20networks%20(NN)%20to%20provide%20approximate%2C%20yet%20expressive%20constructs%20for%20the%20unavailable%20likelihood%20function%20and%20the%20posterior%20distribution.%20However%2C%20the%20trade-off%20between%20accuracy%20and%20computational%20demand%20leaves%20much%20space%20for%20improvement.%20In%20this%20work%2C%20we%20propose%20an%20alternative%20that%20provides%20both%20approximations%20to%20the%20likelihood%20and%20the%20posterior%20distribution%2C%20using%20structured%20mixtures%20of%20probability%20distributions.%20Our%20approach%20produces%20accurate%20posterior%20inference%20when%20compared%20to%20state-of-the-art%20NN-based%20SBI%20methods%2C%20even%20for%20multimodal%20posteriors%2C%20while%20exhibiting%20a%20much%20smaller%20computational%20footprint.%20We%20illustrate%20our%20results%20on%20several%20benchmark%20models%20from%20the%20SBI%20literature%20and%20on%20a%20biological%20model%20of%20the%20translation%20kinetics%20after%20mRNA%20transfection.)
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