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Found 23 papers, 2 papers with code
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation
Bayesian networks (BNs) are a foundational model in machine learning and causal inference.
Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare.
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered.
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering
Rooted in the linear mixed-effects models framework and tailored for hierarchical data, this novel approach demonstrates enhanced BN learning.
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available.
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task.
fairml: A Statistician's Take on Fair Machine Learning Modelling
The adoption of machine learning in applications where it is crucial to ensure fairness and accountability has led to a large number of model proposals in the literature, largely formulated as optimisation problems with constraints reducing or eliminating the effect of sensitive attributes on the response.
Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets
We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks.
Comments on: "Hybrid Semiparametric Bayesian Networks"
Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks" by David Atienza, Pedro Larranaga and Concha Bielza (TEST, 2022).
Achieving Fairness with a Simple Ridge Penalty
In this paper we present a general framework for estimating regression models subject to a user-defined level of fairness.
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.
A Bayesian Hierarchical Score for Structure Learning from Related Data Sets
In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD).
A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks
Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention.
Learning Bayesian Networks from Incomplete Data with the Node-Average Likelihood
Bayesian network (BN) structure learning from complete data has been extensively studied in the literature.
Bayesian Network Models for Incomplete and Dynamic Data
Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way.
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches.
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle
We will use this connection to show that BDeu should not be used for structure learning from sparse data, since it violates the maximum relative entropy principle; and that it is also problematic from a more classic Bayesian model selection perspective, because it produces Bayes factors that are sensitive to the value of its only hyperparameter.
Beyond Uniform Priors in Bayesian Network Structure Learning
For discrete Bayesian networks, the canonical choice for a posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal likelihood with a uniform (U) graph prior, which assumes a uniform prior both on the network structures and on the parameters of the networks.
An Empirical-Bayes Score for Discrete Bayesian Networks
For discrete Bayesian networks, the canonical choice for a posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal likelihood with a uniform (U) graph prior (Heckerman et al., 1995).
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package
It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios.
Graphical Modelling in Genetics and Systems Biology
Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century.
On Identifying Significant Edges in Graphical Models of Molecular Networks
Results: The improved performance of the proposed approach is demonstrated across the synthetic data sets using sensitivity, specificity and accuracy as performance metrics.
Learning Bayesian Networks with the bnlearn R Package
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables.
