A survey of Bayesian Network structure learning
Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
[HTML][HTML] Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
Abstract Numerous Bayesian Network (BN) structure learning algorithms have been
proposed in the literature over the past few decades. Each publication makes an empirical …
proposed in the literature over the past few decades. Each publication makes an empirical …
[HTML][HTML] Effective and efficient structure learning with pruning and model averaging strategies
Learning the structure of a Bayesian Network (BN) with score-based solutions involves
exploring the search space of possible graphs and moving towards the graph that …
exploring the search space of possible graphs and moving towards the graph that …
Hard and soft EM in Bayesian network learning from incomplete data
Incomplete data are a common feature in many domains, from clinical trials to industrial
applications. Bayesian networks (BNs) are often used in these domains because of their …
applications. Bayesian networks (BNs) are often used in these domains because of their …
Tuning structure learning algorithms with out-of-sample and resampling strategies
K Chobtham, AC Constantinou - Knowledge and Information Systems, 2024 - Springer
One of the challenges practitioners face when applying structure learning algorithms to their
data involves determining a set of hyperparameters; otherwise, a set of hyperparameter …
data involves determining a set of hyperparameters; otherwise, a set of hyperparameter …
[PDF][PDF] Approximate Inference in Logical Credal Networks.
Abstract Logical Credal Networks or LCNs is a recent probabilistic logic designed for
effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN …
effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN …
Benchpress: A scalable and versatile workflow for benchmarking structure learning algorithms
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 …
generating mechanism is a fundamental problem in many empirical sciences. Probabilistic …
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Abstract In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning
and inference. However, Markov equivalence class considerations mean it is not always …
and inference. However, Markov equivalence class considerations mean it is not always …
The impact of variable ordering on Bayesian Network Structure Learning
NK Kitson, AC Constantinou - Data Mining and Knowledge Discovery, 2024 - Springer
Abstract Causal Bayesian Networks (CBNs) provide an important tool for reasoning under
uncertainty with potential application to many complex causal systems. Structure learning …
uncertainty with potential application to many complex causal systems. Structure learning …
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
K Chobtham, AC Constantinou - … on Probabilistic Graphical …, 2022 - proceedings.mlr.press
Discovering and parameterising latent confounders represent important and challenging
problems in causal structure learning and density estimation respectively. In this paper, we …
problems in causal structure learning and density estimation respectively. In this paper, we …