4 papers
Efficient Learning of Stationary Diffusions with Stein-type Discrepancies
Fabian Bleile, Sarah Lumpp, Mathias Drton
Learning a stationary diffusion amounts to estimating the parameters of a stochastic differential equation whose stationary distribution matches a target distribution. We build on…
On weak convergence of Gaussian conditional distributions
Sarah Lumpp, Mathias Drton
Weak convergence of joint distributions generally does not imply convergence of conditional distributions. In particular, conditional distributions need not converge when joint Gau…
Conditional Independence in Stationary Diffusions
Tobias Boege, Mathias Drton, Benjamin Hollering +3
Stationary distributions of multivariate diffusion processes have recently been proposed as probabilistic models of causal systems in statistics and machine learning. Motivated by…
Faithlessness in Gaussian graphical models
Mathias Drton, Leonard Henckel, Benjamin Hollering +1
The implication problem for conditional independence (CI) asks whether the fact that a probability distribution obeys a given finite set of CI relations implies that a further CI s…