prior sensitivity
Definition
Prior sensitivity refers to the degree to which Bayesian inference outcomes, particularly the inclusion Bayes factor used for conditional independence testing, are influenced by the specification of prior distributions on network parameters and network structure. The scale of the prior distribution on partial correlations is a critical dimension of prior sensitivity, as even small variations can substantially alter the Bayes factor's ability to distinguish between the presence and absence of an edge, necessitating careful, evidence-based prior specification to prevent misleading conclusions about conditional independence relationships.
Sources: Sekulovski et al. (2024)
Related Terms
Applications
Prior Sensitivity and Conditional Independence Testing
Prior sensitivity directly affects the validity of conditional independence testing, as the inclusion Bayes factor used to test for conditional independence between pairs of variables depends critically on how prior distributions are specified. Understanding prior sensitivity is essential for researchers applying Bayesian methods, ensuring that evidence for or against conditional independence relationships is robust rather than artifacts of prior choices.
Sources: Sekulovski et al. (2024)
Prior Sensitivity and Network Structure
The configuration of edges in a network's structure is governed by prior specifications for both the network structure itself and the edge weight parameters, and prior sensitivity determines how these specifications influence inference about which edges should be present. Different choices of priors on network parameters and structure can substantially alter conclusions about conditional independence relationships.
Sources: Sekulovski et al. (2024)
