Carlos Améndola
Carlos Améndola
Assistant Professor, TU Berlin
Verified email at - Homepage
Cited by
Cited by
Moment varieties of Gaussian mixtures
C Améndola, JC Faugčre, B Sturmfels
Algebraic Statistics 7 (1), 14-28, 2016
Invariant theory and scaling algorithms for maximum likelihood estimation
C Améndola, K Kohn, P Reichenbach, A Seigal
SIAM Journal on Applied Algebra and Geometry 5 (2), 304-337, 2021
The maximum likelihood degree of toric varieties
C Améndola, N Bliss, I Burke, CR Gibbons, M Helmer, S Hoşten, ED Nash, ...
Journal of Symbolic Computation 92, 222-242, 2019
Algebraic identifiability of Gaussian mixtures
C Améndola, K Ranestad, B Sturmfels
International mathematics research notices 2018 (21), 6556-6580, 2018
Maximum number of modes of Gaussian mixtures
C Améndola, A Engström, C Haase
Information and Inference: A Journal of the IMA, 2019
Maximum likelihood estimates for Gaussian mixtures are transcendental
C Améndola, M Drton, B Sturmfels
International Conference on Mathematical Aspects of Computer and Information …, 2015
Varieties of signature tensors
C Améndola, P Friz, B Sturmfels
Forum of Mathematics, Sigma 7, e10, 2019
Conditional independence in max-linear Bayesian networks
C Améndola, C Klüppelberg, S Lauritzen, NM Tran
The Annals of Applied Probability 32 (1), 1-45, 2022
The maximum likelihood degree of linear spaces of symmetric matrices
C Améndola, L Gustafsson, K Kohn, O Marigliano, A Seigal
Le Matematiche 76 (2), 2021
Discrete Gaussian distributions via theta functions
D Agostini, C Améndola
SIAM Journal on Applied Algebra and Geometry 3 (1), 1-30, 2019
Solving parameterized polynomial systems with decomposable projections
C Améndola, J Lindberg, JI Rodriguez
arXiv preprint arXiv:1612.08807, 2016
Structure learning for cyclic linear causal models
C Améndola, P Dettling, M Drton, F Onori, J Wu
Conference on Uncertainty in Artificial Intelligence, 999-1008, 2020
Maximum likelihood estimation of toric Fano varieties
C Améndola, D Kosta, K Kubjas
Algebraic Statistics 11 (1), 5-30, 2020
Toric invariant theory for maximum likelihood estimation in log-linear models
C Améndola, K Kohn, P Reichenbach, A Seigal
Algebraic Statistics 12 (2), 187-211, 2021
Markov equivalence of max-linear Bayesian networks
C Améndola, B Hollering, S Sullivant, N Tran
Uncertainty in Artificial Intelligence, 1746-1755, 2021
Computing tropical varieties in Macaulay2
C Améndola, K Kohn, S Lamboglia, D Maclagan, B Smith, J Sommars, ...
arXiv preprint arXiv:1710.10651, 2017
Algebraic Statistics of Gaussian Mixtures
C Améndola
TU Berlin, 2017
Estimating Gaussian mixtures using sparse polynomial moment systems
J Lindberg, C Améndola, JI Rodriguez
arXiv preprint arXiv:2106.15675, 2021
Identifiability in continuous Lyapunov models
P Dettling, R Homs, C Améndola, M Drton, NR Hansen
SIAM Journal on Matrix Analysis and Applications 44 (4), 1799-1821, 2023
Likelihood geometry of correlation models
C Améndola, P Zwiernik
Le Matematiche 76 (2), 559-583, 2021
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