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F. T. Burbrink

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Evolutionary Biology· 1

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Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

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G. G. Mazzochinni· 1 shared M. Gehara· 1 shared

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G. G. Mazzochinni

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Evolutionary BiologybioRxivPreprintDec 4, 2020

PipeMaster: inferring population divergence and demographic history with approximate Bayesian computation and supervised machine-learning in R

M. Gehara, G. G. Mazzochinni, F. T. Burbrink

PipeMaster uses ABC and SML to infer population divergence and demographic history, finding SML more efficient and accurate than ABC in three empirical examples.

DOI 10.1101/2020.12.04.410670

statistical modelmachine learning (classical)

𝕏 PostBluesky