Work

Dynamic Bayesian Networks with Conditional Dynamics in Edge Addition and Deletion

Year: 2024

Type: preprint

Abstract: This study presents a dynamic Bayesian network framework that facilitates intuitive gradual edge changes. We use two conditional dynamics to model the edge addition and deletion, and edge selection separately. Unlike previous research that uses a mixture network approach, which restricts the number of possible edge changes, or structural priors to induce gradual changes, which can lead to unclear network evolution, our model induces more frequent and intuitive edge change dynamics. We employ Markov chain Monte Carlo (MCMC) sampling to estimate the model structures and parameters and demonstrate the model's effectiveness in a portfolio selection application.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.08965

Authors: Chan, Lupe S. H., Chu, Amanda M. Y., So, Mike K. P.

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403662283

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