A New Partially Segment-Wise Coupled Piece-Wise Linear Regression Model for Statistical Network Structure Inference

Mahdi Shafiee Kamalabad, Marco Grzegorczyk*

*Corresponding author for this work

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We propose a new non-homogeneous dynamic Bayesian network with partially segment-wise sequentially coupled network parameters. The idea is to infer the segmentation of a time series of network data using multiple changepoint processes, and to model the data in each segment by linear regression models. The conventional uncoupled models infer the network interaction parameters for each segment separately, without any systematic information-sharing among segments. More recently, it was proposed to couple the network interaction parameters sequentially among segments. The idea is to enforce the parameters of any segment to stay similar to those of the previous segment. This coupling mechanism can be disadvantageous, as it enforces coupling and does not feature any options to uncouple. We propose a new consensus model that infers for each individual segment whether it should be coupled to (or better should stay uncoupled from) the preceding one.

Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics
Subtitle of host publication15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018, Revised Selected Papers
EditorsMaria Raposo, Paulo Ribeiro, Susana Sério, Antonino Staiano, Angelo Ciaramella
Number of pages14
ISBN (Electronic)978-3-030-34585-3
ISBN (Print)978-3-030-34584-6
Publication statusPublished - 2020
Event15th International Meeting, CIBB 2018 - Caparica, Portugal
Duration: 6-Sept-20188-Sept-2018

Publication series

NameLecture Notes in Bioinformatics


Conference15th International Meeting, CIBB 2018


  • Bayesian piece-wise linear regression
  • Dynamic Bayesian networks
  • Network structure learning
  • Partial segment-wise coupling

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