Modeling Electrical Distribution Networks with Inhomogeneous Galton-Watson Trees

In this paper we consider inhomogeneous Galton-Watson trees, and derive various moments for such processes: the number of vertices, the number of leaves, and the height of the tree. Also we propose a simple condition of finiteness. We use these processes to model a data set consisting of electrical distribution networks, where we make a flexible framework for formulating models through the mean and variance of the offspring distributions. Furthermore, we introduce two mixture distributions as offspring distributions to reflect the particular form of the data. For estimation we use maximum likelihood estimation.

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Publication Details

Journal
Methodology And Computing In Applied Probability
Published
2025-11-21
DOI
https://doi.org/10.1007/s11009-025-10230-1
Primary Topic
Bayesian Methods and Mixture Models
Type
article
Field-Weighted Citation Impact
0.00

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article

Modeling Electrical Distribution Networks with Inhomogeneous Galton-Watson Trees

Troels Pedersen, Jakob Gulddahl Rasmussen, Rasmus Løvenstein Olsen
Methodology And Computing In Applied Probability
Bayesian Methods and Mixture Models
article

Modeling Electrical Distribution Networks with Inhomogeneous Galton-Watson Trees

Troels Pedersen, Jakob Gulddahl Rasmussen, Rasmus Løvenstein Olsen
article en

Abstract

In this paper we consider inhomogeneous Galton-Watson trees, and derive various moments for such processes: the number of vertices, the number of leaves, and the height of the tree. Also we propose a simple condition of finiteness. We use these processes to model a data set consisting of electrical distribution networks, where we make a flexible framework for formulating models through the mean and variance of the offspring distributions. Furthermore, we introduce two mixture distributions as offspring distributions to reflect the particular form of the data. For estimation we use maximum likelihood estimation.

Methodology And Computing In Applied ProbabilityVol. 27(4)
Aalborg University (DK)
Innovationsfonden, European Commission, Bundesministerium für Bildung und Forschung, Eurostars
Openalex Percentile: Top 81%
Bayesian Methods and Mixture Models
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