40 citations · 47 across the 7 of their papers we have counts for
7 papers
Asymptotics for Model Selection in Probabilistic Principal Component Analysis
Mathias Drton, Andrew McCormack, Daniel Windisch
The probabilistic formulation of principal component analysis promises statistically grounded solutions to the problem of selecting the number of principal components. However, dev…
Cost-Aware Optimized Front-Door Experimental Design
Leopold Mareis, Mathias Drton
Causal effect estimation often succeeds cost-constrained sequential data collection. This work considers multivariate linear front-door models with arbitrary unobserved confounding…
Singular Learning Theory for Factor Analysis
Mathias Drton, Elizabeth Gross, Dimitra Kosta +4
Watanabe's singular learning theory provides a framework for asymptotic analysis of Bayesian model selection for statistical models with singularities, where traditional statistica…
Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity
David Strieder, Mathias Drton
A fundamental challenge of scientific research is inferring causal relations based on observed data. One commonly used approach involves utilizing structural causal models that pos…
Dual Likelihood for Causal Inference under Structure Uncertainty
David Strieder, Mathias Drton
Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postu…
Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances
David Strieder, Mathias Drton
Inferring the effect of interventions within complex systems is a fundamental problem of statistics. A widely studied approach employs structural causal models that postulate noisy…
A Mutagenetic Tree Hidden Markov Model for Longitudinal Clonal HIV Sequence Data
Niko Beerenwinkel, Mathias Drton
RNA viruses provide prominent examples of measurably evolving populations. In HIV infection, the development of drug resistance is of particular interest, because precise predictio…