Iheb Marouani

Work05

Computational Belief Modeling

In productionB.Sc. thesis

Thesis (1.0, Musslick Lab): regularized belief networks across six survey contexts, encoded in a Neo4j knowledge graph, explorable live.

Sample data

Network stability 0.74

TrustRiskWorryComplianceKnowledge

Solid edges are positive partial correlations, dashed are negative; thickness is absolute weight. Edge weights and stability are invented.

PrototypeAn interface sketch of this system, not a screenshot. Every figure, name and record in it is invented.
Built with
Python, R, qgraph / bootnet / glasso, Neo4j, Streamlit

The problem

How is a belief network structured, and how does that structure shift across contexts like institutional trust? The thesis modeled belief-affect-behavior structure in COSMO COVID-19 survey data.

The system

A reproducible Python/R pipeline estimates Gaussian Graphical Models (EBICglasso) across six survey contexts, computes bootstrap edge stability and focal-belief embeddedness, and encodes every network instance into a Neo4j belief knowledge graph for structured cross-context comparison.

How it is built

Analysis in Python and R (qgraph, bootnet, glasso); Cypher import pipeline for the knowledge graph; public Streamlit app for interactive exploration including a Conditional Mean Shift explorer.

  1. 1

    COSMO survey data

    Belief-affect-behaviour responses across six survey contexts.

  2. 2

    EBICglasso estimation

    Gaussian Graphical Models estimated per context in Python and R.

  3. 3

    Stability analysis

    Bootstrap edge stability and focal-belief embeddedness computed.

  4. 4

    Neo4j knowledge graph

    Every network instance encoded via a Cypher import pipeline for cross-context comparison.

  5. 5

    Streamlit explorer

    Public app for interactive exploration, including a Conditional Mean Shift explorer.

OutcomeGraded 1.0. Live demo public.

SourceLive