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Publication search results
found 80 matches
- 2019
- Antonino Abbruzzo, Ivan Vujacic, Angelo M. Mineo, Ernst C. Wit:
Selecting the tuning parameter in penalized Gaussian graphical models. Stat. Comput. 29(3): 559-569 (2019) - Andrés M. Alonso, Daniel Peña:
Clustering time series by linear dependency. Stat. Comput. 29(4): 655-676 (2019) - Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth:
Control variates for stochastic gradient MCMC. Stat. Comput. 29(3): 599-615 (2019) - Eustasio del Barrio, Juan Antonio Cuesta-Albertos, Carlos Matrán, Agustín Mayo-Íscar:
Robust clustering tools based on optimal transportation. Stat. Comput. 29(1): 139-160 (2019) - Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen, Philipp Hennig:
Probabilistic linear solvers: a unifying view. Stat. Comput. 29(6): 1249-1263 (2019) - Moritz Berger, Gerhard Tutz, Matthias Schmid:
Tree-structured modelling of varying coefficients. Stat. Comput. 29(2): 217-229 (2019) - Andrew J. Black:
Importance sampling for partially observed temporal epidemic models. Stat. Comput. 29(4): 617-630 (2019) - Joshua J. Bon, Kevin Murray, Berwin A. Turlach:
Fitting monotone polynomials in mixed effects models. Stat. Comput. 29(1): 79-98 (2019) - Jian Cao, Marc G. Genton, David E. Keyes, George M. Turkiyyah:
Hierarchical-block conditioning approximations for high-dimensional multivariate normal probabilities. Stat. Comput. 29(3): 585-598 (2019) - Minwoo Chae, Ryan Martin, Stephen G. Walker:
On an algorithm for solving Fredholm integrals of the first kind. Stat. Comput. 29(4): 645-654 (2019) - Xi Chen, Michael P. Hobson, Saptarshi Das, Paul Gelderblom:
Improving the efficiency and robustness of nested sampling using posterior repartitioning. Stat. Comput. 29(4): 835-850 (2019) - Oksana A. Chkrebtii, David A. Campbell:
Adaptive step-size selection for state-space probabilistic differential equation solvers. Stat. Comput. 29(6): 1285-1295 (2019) - Marco Corneli, Charles Bouveyron, Pierre Latouche, Fabrice Rossi:
The dynamic stochastic topic block model for dynamic networks with textual edges. Stat. Comput. 29(4): 677-695 (2019) - Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes:
A novel Bayesian approach for latent variable modeling from mixed data with missing values. Stat. Comput. 29(5): 977-993 (2019) - Ruifei Cui, Perry Groot, Tom Heskes:
Learning causal structure from mixed data with missing values using Gaussian copula models. Stat. Comput. 29(2): 311-333 (2019) - Michiel Debruyne, Sebastiaan Höppner, Sven Serneels, Tim Verdonck:
Outlyingness: Which variables contribute most? Stat. Comput. 29(4): 707-723 (2019) - Matthew C. Edwards, Renate Meyer, Nelson Christensen:
Bayesian nonparametric spectral density estimation using B-spline priors. Stat. Comput. 29(1): 67-78 (2019) - Martin Ehler, Manuel Gräf, Chris J. Oates:
Optimal Monte Carlo integration on closed manifolds. Stat. Comput. 29(6): 1203-1214 (2019) - Javier Espinosa, Christian Hennig:
A constrained regression model for an ordinal response with ordinal predictors. Stat. Comput. 29(5): 869-890 (2019) - Michael Fop, Thomas Brendan Murphy, Luca Scrucca:
Model-based clustering with sparse covariance matrices. Stat. Comput. 29(4): 791-819 (2019) - Gersende Fort, Edouard Ollier, Adeline Samson:
Stochastic proximal-gradient algorithms for penalized mixed models. Stat. Comput. 29(2): 231-253 (2019) - Eduardo García-Portugués, Michael Sørensen, Kanti V. Mardia, Thomas Hamelryck:
Langevin diffusions on the torus: estimation and applications. Stat. Comput. 29(1): 1-22 (2019) - Michael B. Giles, Takashi Goda:
Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI. Stat. Comput. 29(4): 739-751 (2019) - Colin S. Gillespie, Richard J. Boys:
Efficient construction of Bayes optimal designs for stochastic process models. Stat. Comput. 29(4): 697-706 (2019) - Mark A. Girolami, Ilse C. F. Ipsen, Chris J. Oates, Art B. Owen, Timothy John Sullivan:
Editorial: special edition on probabilistic numerics. Stat. Comput. 29(6): 1181-1183 (2019) - Andrew Golightly, Chris Sherlock:
Efficient sampling of conditioned Markov jump processes. Stat. Comput. 29(5): 1149-1163 (2019) - Michael Grabchak:
Rejection sampling for tempered Lévy processes. Stat. Comput. 29(3): 549-558 (2019) - Jiaying Gu, Fei Fu, Qing Zhou:
Penalized estimation of directed acyclic graphs from discrete data. Stat. Comput. 29(1): 161-176 (2019) - Sabrina Guastavino, Federico Benvenuto:
A consistent and numerically efficient variable selection method for sparse Poisson regression with applications to learning and signal recovery. Stat. Comput. 29(3): 501-516 (2019) - David Gunawan, Minh-Ngoc Tran, Kosuke Suzuki, Josef Dick, Robert Kohn:
Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins. Stat. Comput. 29(5): 933-946 (2019)
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