PYLFIRE: Python implementation of likelihood-free inference by ratio estimation.
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ABSTRACT:
SUBMITTER: Kokko J
PROVIDER: S-EPMC7041362 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
PYLFIRE: Python implementation of likelihood-free inference by ratio estimation.
Kokko Jan J Remes Ulpu U Thomas Owen O Pesonen Henri H Corander Jukka J
Wellcome open research 20191210
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 ...[more]
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