Automatic differentiation is no panacea for phylogenetic gradient computation

Autor: Fourment, Mathieu, Swanepoel, Christiaan J., Galloway, Jared G., Ji, Xiang, Gangavarapu, Karthik, Suchard, Marc A., Matsen IV, Frederick A.

Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via automatic differentiation implemen

Externí odkaz: http://arxiv.org/abs/2211.02168

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Efficient ancestry and mutation simulation with msprime 1.0

Autor: Baumdicker, Franz, Bisschop, Gertjan, Goldstein, Daniel, Gower, Graham, Ragsdale, Aaron P., Tsambos, Georgia, Zhu, Sha, Eldon, Bjarki, Ellerman, Castedo E., Galloway, Jared G., Gladstein, Ariella L., Gorjanc, Gregor, Guo, Bing, Jeffery, Ben, Kretzschmar, Warren W., Lohse, Konrad, Matschiner, Michael, Nelson, Dominic, Pope, Nathaniel S., Quinto-Cortés, Consuelo D., Rodrigues, Murillo F., Saunack, Kumar, Sellinger, Thibaut, Thornton, Kevin, van Kemenade, Hugo, Wohns, Anthony W., Wong, H. Yan, Gravel, Simon, Kern, Andrew D., Koskela, Jere, Ralph, Peter L., Kelleher, Jerome

Publikováno v: Baumdicker, F, Bisschop, G, Goldstein, D, Gower, G, Ragsdale, A P, Tsambos, G, Zhu, S, Eldon, B, Ellerman, C E, Galloway, J G, Gladstein, A L, Gorjanc, G, Guo, B, Jeffery, B, Kretzschmar, W W, Lohse, K, Matschiner, M, Nelson, D, Pope, N S, Quinto-Cortés, C D, Rodrigues, M F, Saunack, K, Sellinger, T, Thornton, K, van Kemenade, H, Wohns, A W, Wong, H Y, Gravel, S, Kern, A D, Koskela, J, Ralph, P L & Kelleher, J 2021 ' Efficient ancestry and mutation simulation with msprime 1.0 ' bioRxiv . https://doi.org/10.1101/2021.08.31.457499

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3094::a89c60bf2807d74b4700fc88be43f9fa
https://hdl.handle.net/20.500.11820/86501e3a-b35c-478f-bbdf-ca90bba30bfd