Hans J. Skaug

Oper. Res. Forum, December, 2023

2022

Epistemic uncertainty quantification in deep learning classification by the Delta method.

[BibT_eX]

[DOI]

Neural Networks, 2022

2021

A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification.

[BibT_eX]

[DOI]

CoRR, 2021

2019

On the Delta Method for Uncertainty Approximation in Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2019

Efficient Computation of Hessian Matrices in TensorFlow.

[BibT_eX]

[DOI]

CoRR, 2019

2018

On the application of improved symplectic integrators in Hamiltonian Monte Carlo.

[BibT_eX]

[DOI]

Janne Mannseth

Tore Selland Kleppe

Commun. Stat. Simul. Comput., 2018

2017

glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling.

[BibT_eX]

[DOI]

R J., 2017

2016

Bandwidth selection in pre-smoothed particle filters.

[BibT_eX]

[DOI]

Tore Selland Kleppe

Stat. Comput., 2016

2014

Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2014

A flexible and automated likelihood based framework for inference in stochastic volatility models.

[BibT_eX]

[DOI]

Jun Yu

Comput. Stat. Data Anal., 2014

2012

AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models.

[BibT_eX]

[DOI]

Optim. Methods Softw., 2012

Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling.

[BibT_eX]

[DOI]

Tore Selland Kleppe

Hans Julius Skaug

Comput. Stat. Data Anal., 2012

2006

Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models.

[BibT_eX]

[DOI]