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Thus, we can sample from the posterior densityzij,t by drawingκ ij,t ∼ U(0,1)then computing zij,t =µ ij,t +F −1 u wij,t +κ ijt(1−w ij,t −F u(µij,t) · 2024

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A Bayesian Dynamic Latent Space Model for Weighted Networks

stat.ME · 2026-03-25 · unverdicted · novelty 7.0

A Bayesian dynamic latent space model is introduced for weighted temporal networks with time-varying features, excess zeros, and an efficient multi-move sampler new to the network literature.

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  • A Bayesian Dynamic Latent Space Model for Weighted Networks stat.ME · 2026-03-25 · unverdicted · none · ref 10

    A Bayesian dynamic latent space model is introduced for weighted temporal networks with time-varying features, excess zeros, and an efficient multi-move sampler new to the network literature.