Zobrazeno 1 - 10
of 293
pro vyhledávání: '"Kuipers, Jack"'
Causal Bayesian networks are widely used tools for summarising the dependencies between variables and elucidating their putative causal relationships. Learning networks from data is computationally hard in general. The current state-of-the-art approa
Externí odkaz:
http://arxiv.org/abs/2406.15012
The G-Wishart distribution is an essential component for the Bayesian analysis of Gaussian graphical models as the conjugate prior for the precision matrix. Evaluating the marginal likelihood of such models usually requires computing high-dimensional
Externí odkaz:
http://arxiv.org/abs/2404.06803
Causal discovery and inference from observational data is an essential problem in statistics posing both modeling and computational challenges. These are typically addressed by imposing strict assumptions on the joint distribution such as linearity.
Externí odkaz:
http://arxiv.org/abs/2402.00623
Clustering algorithms may unintentionally propagate or intensify existing disparities, leading to unfair representations or biased decision-making. Current fair clustering methods rely on notions of fairness that do not capture any information on the
Externí odkaz:
http://arxiv.org/abs/2312.09061
Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network. The model allows the description of continuous join
Externí odkaz:
http://arxiv.org/abs/2306.11380
Autor:
Novaes, Marcel, Kuipers, Jack
We develop a semiclassical approach for the statistics of the time delay in quantum chaotic systems in the presence of a tunnel barrier, for broken time-reversal symmetry. Results are obtained as asymptotic series in powers of the reflectivity of the
Externí odkaz:
http://arxiv.org/abs/2302.09030
Autor:
Kuipers, Jack, Moffa, Giusi
Describing the causal relations governing a system is a fundamental task in many scientific fields, ideally addressed by experimental studies. However, obtaining data under intervention scenarios may not always be feasible, while discovering causal r
Externí odkaz:
http://arxiv.org/abs/2205.02602
Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidden variables. While inference of the marginal probability distribution
Externí odkaz:
http://arxiv.org/abs/2112.09217
Learning the graphical structure of Bayesian networks is key to describing data-generating mechanisms in many complex applications but poses considerable computational challenges. Observational data can only identify the equivalence class of the dire
Externí odkaz:
http://arxiv.org/abs/2112.09036
Describing the relationship between the variables in a study domain and modelling the data generating mechanism is a fundamental problem in many empirical sciences. Probabilistic graphical models are one common approach to tackle the problem. Learnin
Externí odkaz:
http://arxiv.org/abs/2107.03863