Improving Bayesian Network Structure Learning in the Presence of Measurement Error
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the reasons why the synthetic performance of these algorithms often overestimates real-world performance. This paper describes an algorithm that can be added as an additional learning phase at the end of any structure learning algorithm, and serves as a correction learning phase that removes potential false positive edges. The results show that the proposed correction algorithm successfully improves the graphical score of four well-established structure learning algorithms spanning different classes of learning in the presence of measurement error.
Code (1)
Similar Papers 제목 키워드 기반
Tree-Based Predictive Models for Noisy Input Data
Measurement error is prevalent across all domains of scientific research where only imprecise observations, rather than the true underlying values, can be obtained. For example, estimates of human microbiome diversity ar…
Variational-Based Nonlinear Bayesian Filtering with Biased Observations
State estimation of dynamical systems is crucial for providing new decision-making and system automation information in different applications. However, the assumptions on the standard computational models for sensor mea…
Decision MakingState EstimationState Space ModelsKoopman Spectral Analysis from Noisy Measurements based on Bayesian Learning and Kalman Smoothing
Koopman spectral analysis plays a crucial role in understanding and modeling nonlinear dynamical systems as it reveals key system behaviors and long-term dynamics. However, the presence of measurement noise poses a signi…
Towards safe Bayesian optimization with Wiener kernel regression
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, the performance of BO crucially relies on…
Bayesian OptimizationregressionLearning Bayesian and Markov Networks with an Unreliable Oracle
We study constraint-based structure learning of Markov networks and Bayesian networks in the presence of an unreliable conditional independence oracle that makes at most a bounded number of errors. For Markov networks, w…