11 citations · 11 across the 2 of their papers we have counts for
4 papers
Causal computations in Semi Markovian Structural Causal Models using divide and conquer
Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth +1
Recently, Bjøru et al. proposed a novel divide-and-conquer algorithm for bounding counterfactual probabilities in structural causal models (SCMs). They assumed that the SCMs were l…
Probabilistic Models with Deep Neural Networks
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth +2
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very re…
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
Javier Cózar, Rafael Cabañas, Antonio Salmerón +1
InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other lib…
Bayesian Models of Data Streams with Hierarchical Power Priors
Andres Masegosa, Thomas D. Nielsen, Helge Langseth +3
Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating and adapt…