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T1 - PYLFIRE : Python implementation of likelihood-free inference by ratio estimation
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UR - http://hdl.handle.net/10138/326329
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A1 - Kokko, J.; Remes, U.; Thomas, Owen; Pesonen, H.; Corander, J.
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Y1 - 2019
LA - eng
AB - Likelihood-free inference for simulator-based models is an emerging methodological branch of statistics which has attracted considerable attention in applications across diverse fields such as population genetics, astronomy and economics. Recently, the power of statistical classifiers has been harnessed in likelihood-free inference to obtain either point estimates or even posterior distributions of model parameters. Here we introduce PYLFIRE, an open-source Python implementation of the inference...
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KW - 112 Statistics and probability; density-ratio estimation; likelihood-free inference; logistic regression; summary statistics selection
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