Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters.

ABSTRACT:

Motivation

Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular tool for learning networks with time-varying interaction parameters. A multiple changepoint process is used to divide the data into disjoint segments and the network interaction parameters are assumed to be segment-specific. The objective is to infer the network structure along with the segmentation and the segment-specific parameters from the data. The conventional (uncoupled) NH-DBNs do not allow for information exchange among segments, and the interaction parameters have to be learned separately for each segment. More advanced coupled NH-DBN models allow the interaction parameters to vary but enforce them to stay similar over time. As the enforced similarity of the network parameters can have counter-pr

SUBMITTER: Shafiee Kamalabad M 

PROVIDER: S-EPMC7703764 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature