Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Moffa, Giusi"'
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
Autor:
Bartels, Christian, Scauda, Martina, Coello, Neva, Dumortier, Thomas, Bornkamp, Bjoern, Moffa, Giusi
Non-linear mixed effects modeling and simulation (NLME M&S) is evaluated to be used for standardization with longitudinal data in presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyz
Externí odkaz:
http://arxiv.org/abs/2404.19325
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
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:
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
The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distr
Externí odkaz:
http://arxiv.org/abs/2105.00488