Bayesian Sparse Factor Models and DAGs Inference and Comparison
In this paper we present a novel approach to learn directed acyclic graphs (DAG) and factor models within the same framework while also allowing for model comparison between them. For this purpose, we exploit the connection between factor models and DAGs to propose Bayesian hierarchies based on spike and slab priors to promote sparsity, heavy-tailed priors to ensure identifiability and predictive densities to perform the model comparison. We require identifiability to be able to produce variable orderings leading to valid DAGs and sparsity to learn the structures. The effectiveness of our approach is demonstrated through extensive experiments on artificial and biological data showing that our approach outperform a number of state of the art methods.
Code (0)
등록된 구현이 없습니다.
Tasks
validSimilar Papers 제목 키워드 기반
Optimizing regularized Cholesky score for order-based learning of Bayesian networks
Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, anneali…
BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs
Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estim…
Causal DiscoveryBCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
A structural equation model (SEM) is an effective framework to reason over causal relationships represented via a directed acyclic graph (DAG). Recent advances have enabled effective maximum-likelihood point estimation o…
Causal DiscoveryStochastic OptimizationVariational InferenceBayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However, computational challenges arise due to …
Causal DiscoveryVariational InferenceCausal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relati…