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The field of numerical algebraic geometry consists of algorithms for numerically solving systems of polynomial equations. When the system is exact, such as having rational coefficients, the solution set is well-defined. However, for a member of a par
Externí odkaz:
http://arxiv.org/abs/2403.18749
Autor:
Wang, Yu, Cobian, Emma R., Lee, Jubilee, Liu, Fang, Hauenstein, Jonathan D., Schiavazzi, Daniele E.
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational i
Externí odkaz:
http://arxiv.org/abs/2307.04675
Approximating probability distributions can be a challenging task, particularly when they are supported over regions of high geometrical complexity or exhibit multiple modes. Annealing can be used to facilitate this task which is often combined with
Externí odkaz:
http://arxiv.org/abs/2202.00792
Akademický článek
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Publikováno v:
International Journal for Uncertainty Quantification. 13:39-68
Approximating probability distributions can be a challenging task, particularly when they are supported over regions of high geometrical complexity or exhibit multiple modes. Annealing can be used to facilitate this task which is often combined with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8a3ef888d3b9fd3f46cc4c78f77b951
https://doi.org/10.1615/int.j.uncertaintyquantification.2022043110
https://doi.org/10.1615/int.j.uncertaintyquantification.2022043110
