Hans J. Skaug - RankMe

13 papers Journal 13

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Sparse Bayesian learning using TMB (Template Model Builder). Stat. Comput. Ingvild M. Helgøy, Hans J. Skaug, Yushu Li
2023	J	`jnl`	A TMB Approach to Study Spatial Variation in Weather-Generated Claims in Insurance. Oper. Res. Forum Ingrid Sandvig Thorsen, Bård Støve, Hans J. Skaug
2022	J	`jnl`	Epistemic uncertainty quantification in deep learning classification by the Delta method. Neural Networks Geir Kjetil Nilsen, Antonella Zanna Munthe-Kaas, Hans J. Skaug, Morten Brun
2021	J	`jnl`	A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification. CoRR Geir Kjetil Nilsen, Antonella Zanna Munthe-Kaas, Hans J. Skaug, Morten Brun
2019	J	`jnl`	Efficient Computation of Hessian Matrices in TensorFlow. CoRR Geir Kjetil Nilsen, Antonella Zanna Munthe-Kaas, Hans J. Skaug, Morten Brun
2019	J	`jnl`	On the Delta Method for Uncertainty Approximation in Deep Learning. CoRR Geir Kjetil Nilsen, Antonella Zanna Munthe-Kaas, Hans J. Skaug, Morten Brun
2018	J	`jnl`	On the application of improved symplectic integrators in Hamiltonian Monte Carlo. Commun. Stat. Simul. Comput. Janne Mannseth, Tore Selland Kleppe, Hans J. Skaug
2017	J	`jnl`	glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. R J. Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Mächler, Benjamin M. Bolker
2016	J	`jnl`	Bandwidth selection in pre-smoothed particle filters. Stat. Comput. Tore Selland Kleppe, Hans J. Skaug
2014	J	`jnl`	A flexible and automated likelihood based framework for inference in stochastic volatility models. Comput. Stat. Data Anal. Hans J. Skaug, Jun Yu
2014	J	`jnl`	Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method. PLoS Comput. Biol. Simone Vincenzi, Marc Mangel, Alain J. Crivelli, Stephan Munch, Hans J. Skaug
2012	J	`jnl`	AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. David A. Fournier, Hans J. Skaug, Johnoel Ancheta, James Ianelli, Arni Magnusson, Mark N. Maunder, Anders Nielsen, John Sibert
2006	J	`jnl`	Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models. Comput. Stat. Data Anal. Hans J. Skaug, David A. Fournier