52 citations · 167 across the 19 of their papers we have counts for
5 papers · 1 filter
Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
Philipp Reiser, Paul-Christian Bürkner, Anneli Guthke
Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inverse problems, sensitivity analysis, and…
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner +3
The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When de…
Gaussian distributional structural equation models: A framework for modeling latent heteroscedasticity
Luna Fazio, Paul-Christian Bürkner
Accounting for the complexity of psychological theories requires methods that can predict not only changes in the means of latent variables -- such as personality factors, creativi…
DGP-LVM: Derivative Gaussian process latent variable models
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
We develop a framework for derivative Gaussian process latent variable models (DGP-LVMs) that can handle multi-dimensional output data using modified derivative covariance function…
ActionDiffusion: An Action-aware Diffusion Model for Procedure Planning in Instructional Videos
Lei Shi, Paul Bürkner, Andreas Bulling
We present ActionDiffusion -- a novel diffusion model for procedure planning in instructional videos that is the first to take temporal inter-dependencies between actions into acco…