1 citations · 1 across the 6 of their papers we have counts for
6 papers
Posterior uncertainty for kernel density estimates
Dennis Christensen, Torjus Svardal, Leiv Rønneberg +1
Recent work in predictive Bayesian inference has enabled novel Bayesian interpretations of many well-known stochastic one-step-ahead predictive algorithms. In this paper, we study…
Computing Conditional Shapley Values Using Tabular Foundation Models
Lars Henry Berge Olsen, Dennis Christensen
Shapley values have become a cornerstone of explainable AI, but they are computationally expensive to use, especially when features are dependent. Evaluating them requires approxim…
Random irregular histograms
Oskar Høgberg Simensen, Dennis Christensen, Nils Lid Hjort
We propose a new method of histogram construction, providing a fully Bayesian approach to irregular histograms. Our procedure applies Bayesian model selection to a piecewise consta…
Revisiting the Langlie procedure
Dennis Christensen
Introduced in 1962, the Langlie procedure is one of the most popular approaches to sensitivity testing. It aims to estimate an unknown sensitivity distribution based on the outcome…
Asymptotic properties of adaptive designs through differentiability in quadratic mean
Dennis Christensen, Emil Aas Stoltenberg, Nils Lid Hjort
There exist multiple regression applications in engineering, industry and medicine where the outcomes follow an adaptive experimental design in which the next measurement depends o…
perms: Likelihood-free estimation of marginal likelihoods for binary response data in Python and R
Dennis Christensen, Per August Jarval Moen
In Bayesian statistics, the marginal likelihood (ML) is the key ingredient needed for model comparison and model averaging. Unfortunately, estimating MLs accurately is notoriously…