2 papers
stat.ML2025
Covariate Dependent Mixture of Bayesian Networks
Roman Marchant, Dario Draca, Gilad Francis +4
Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on…
stat.ML2024
Optimal Particle-based Approximation of Discrete Distributions (OPAD)
Hadi Mohasel Afshar, Gilad Francis, Sally Cripps
Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribut…