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

Mahdi Shafiee Kamalabad, Marco Grzegorczyk*

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

4 Downloads (Pure)


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.

Originele taal-2English
TitelComputational Intelligence Methods for Bioinformatics and Biostatistics
Subtitel15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018, Revised Selected Papers
RedacteurenMaria Raposo, Paulo Ribeiro, Susana Sério, Antonino Staiano, Angelo Ciaramella
Aantal pagina's14
ISBN van elektronische versie978-3-030-34585-3
ISBN van geprinte versie978-3-030-34584-6
StatusPublished - 2020
Evenement15th International Meeting, CIBB 2018 - Caparica, Portugal
Duur: 6-sep-20188-sep-2018

Publicatie series

NaamLecture Notes in Bioinformatics


Conference15th International Meeting, CIBB 2018

Citeer dit