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Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. A Bayesian network can thus be considered a mechanism for automatically applying Bayes' theorem to…

Artificial Intelligence · Computer Science 2010-11-08 Jianguo Ding

Decomposable dependency models and their graphical counterparts, i.e., chordal graphs, possess a number of interesting and useful properties. On the basis of two characterizations of decomposable models in terms of independence…

Artificial Intelligence · Computer Science 2013-02-08 Luis M. de Campos , Juan F. Huete

There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based…

Machine Learning · Computer Science 2023-07-21 Nis Meinert , Jakob Gawlikowski , Alexander Lavin

Teddy Seidenfeld has been arguing for quite a long time that binary preference models are not powerful enough to deal with a number of crucial aspects of imprecision and indeterminacy in uncertain inference and decision making. It is at his…

Artificial Intelligence · Computer Science 2021-02-23 Jasper De Bock , Gert de Cooman

Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational Bayesian neural…

Machine Learning · Computer Science 2023-12-04 Alexander Möllers , Alexander Immer , Elvin Isufi , Vincent Fortuin

Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems.…

Machine Learning · Computer Science 2021-12-17 Lars Lorch , Jonas Rothfuss , Bernhard Schölkopf , Andreas Krause

Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing…

Artificial Intelligence · Computer Science 2013-02-21 Fahiem Bacchus , Adam J. Grove

We generalize standard credal set models for imprecise probabilities to include higher order credal sets -- confidences about confidences. In doing so, we specify how an agent's higher order confidences (credal sets) update upon observing…

Statistics Theory · Mathematics 2021-07-20 Justus Hibshman , Tim Weninger

We propose a new approach to explain Bayesian Networks. The approach revolves around a new definition of a probabilistic argument and the evidence it provides. We define a notion of independent arguments, and propose an algorithm to extract…

Artificial Intelligence · Computer Science 2021-12-03 Jaime Sevilla

Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. From an imaginery point of view, causal graphs are a helpful tool for representing and infering new causal information. In previous…

Artificial Intelligence · Computer Science 2020-02-07 Eduardo C. Garrido-Merchán , C. Puente , A. Sobrino , J. A. Olivas

Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions on a causal graph, which capture (possibly hypothetical)…

Artificial Intelligence · Computer Science 2021-05-20 Benjie Wang , Clare Lyle , Marta Kwiatkowska

The categorical modeling of Petri nets has received much attention recently. The Dialectica construction has also had its fair share of attention. We revisit the use of the Dialectica construction as a categorical model for Petri nets…

Category Theory · Mathematics 2025-12-24 Elena Di Lavore , Wilmer Leal , Valeria de Paiva

In this paper, we show that coherent sets of gambles and coherent lower and upper previsions can be embedded into the algebraic structure of information algebra. This leads firstly, to a new perspective of the algebraic and logical…

Artificial Intelligence · Computer Science 2021-04-28 Arianna Casanova , Juerg Kohlas , Marco Zaffalon

Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed,…

Machine Learning · Computer Science 2024-10-23 Michele Caprio , Souradeep Dutta , Kuk Jin Jang , Vivian Lin , Radoslav Ivanov , Oleg Sokolsky , Insup Lee

Causal theory is now widely developed with many applications to medicine and public health. However within the discipline of reliability, although causation is a key concept in this field, there has been much less theoretical attention. In…

Artificial Intelligence · Computer Science 2020-02-17 Xuewen Yu , Jim Q. Smith , Linda Nichols

A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal…

Artificial Intelligence · Computer Science 2020-08-04 Marco Zaffalon , Alessandro Antonucci , Rafael Cabañas

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data,…

Methodology · Statistics 2020-11-04 Marco Scutari

In this paper, we show that coherent sets of gambles can be embedded into the algebraic structure of information algebra. This leads firstly, to a new perspective of the algebraic and logical structure of desirability and secondly, it…

Artificial Intelligence · Computer Science 2021-05-26 Juerg Kohlas , Arianna Casanova , Marco Zaffalon

Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks. However,…

Machine Learning · Computer Science 2023-03-08 Ching-Chun Chang

The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabilities via product of parameters are shown to capture…

Artificial Intelligence · Computer Science 2012-06-18 Ydo Wexler , Christopher Meek
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