928 citations · 963 across the 9 of their papers we have counts for
11 papers
Exploring the likelihood surface in multivariate Gaussian mixtures using Hamiltonian Monte Carlo
Francesca Azzolini, Hans Skaug
Multimodality of the likelihood in Gaussian mixtures is a well-known problem. The choice of the initial parameter vector for the numerical optimizer may affect whether the optimize…
A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification
Geir K. Nilsen, Antonella Z. Munthe-Kaas, Hans J. Skaug +1
We validate the recently introduced deep learning classification adapted Delta method by a comparison with the classical Bootstrap. We show that there is a strong linear relationsh…
An information criterion for automatic gradient tree boosting
Berent Ånund Strømnes Lunde, Tore Selland Kleppe, Hans Julius Skaug
An information theoretic approach to learning the complexity of classification and regression trees and the number of trees in gradient tree boosting is proposed. The optimism (tes…
Heritability curves: a local measure of heritability
Geir D. Berentsen, Francesca Azzolini, Hans J. Skaug +2
This paper introduces a new measure of heritability which relaxes the classical assumption that the degree of heritability of a continuous trait can be summarized by a single numbe…
Epistemic Uncertainty Quantification in Deep Learning Classification by the Delta Method
Geir K. Nilsen, Antonella Z. Munthe-Kaas, Hans J. Skaug +1
The Delta method is a classical procedure for quantifying epistemic uncertainty in statistical models, but its direct application to deep neural networks is prevented by the large…
Efficient Computation of Hessian Matrices in TensorFlow
Geir K. Nilsen, Antonella Z. Munthe-Kaas, Hans J. Skaug +1
The Hessian matrix has a number of important applications in a variety of different fields, such as optimzation, image processing and statistics. In this paper we focus on the prac…
Saddlepoint-adjusted inversion of characteristic functions
Berent Å. S. Lunde, Tore S. Kleppe, Hans J. Skaug
For certain types of statistical models, the characteristic function (Fourier transform) is available in closed form, whereas the probability density function has an intractable fo…
Whole genome resequencing reveals diagnostic markers for investigating global migration and hybridization between minke whale species
Ketil Malde, Bjørghild B. Seliussen, María Quintela +11
Background: In the marine environment, where there are few absolute physical barriers, contemporary contact between previously isolated species can occur across great distances, an…
On the application of higher order symplectic integrators in Hamiltonian Monte Carlo
Janne Mannseth, Tore Selland Kleppe, Hans J. Skaug
We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Two integration schemes from Blanes et…
TMB: Automatic Differentiation and Laplace Approximation
Kasper Kristensen, Anders Nielsen, Casper W. Berg +2
TMB is an open source R package that enables quick implementation of complex nonlinear random effect (latent variable) models in a manner similar to the established AD Model Builde…
Bandwidth Selection In Pre-Smoothed Particle Filters
Tore Selland Kleppe, Hans Julius Skaug
For the purpose of maximum likelihood estimation of static parameters, we apply a kernel smoother to the particles in the standard SIR filter for non-linear state space models with…