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Università della Svizzera italiana

Probabilistic models with deep neural networks

Masegosa, Andrés R. ; Cabañas, Rafael ; Langseth, Helge ; Nielsen, Thomas D. ; Salmerón, Antonio

In: Entropy, 2021, vol. 23, no. 1, p. 27 p

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical...

Université de Fribourg

Inference of natural selection from ancient DNA

Dehasque, Marianne ; Ávila‐Arcos, María C. ; Díez‐del‐Molino, David ; Fumagalli, Matteo ; Guschanski, Katerina ; Lorenzen, Eline D. ; Malaspinas, Anna‐Sapfo ; Marques‐Bonet, Tomas ; Martin, Michael D. ; Murray, Gemma G. R. ; Papadopulos, Alexander S. T. ; Therkildsen, Nina Overgaard ; Wegmann, Daniel ; Dalén, Love ; Foote, Andrew D.

In: Evolution Letters, 2020, vol. 4, no. 2, p. 94–108

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct...