A0206
Title: Efficient Gibbs sampling for latent space models
Authors: Roberto Casarin - University Ca' Foscari of Venice (Italy) [presenting]
Antonio Peruzzi - Ca' Foscari University of Venice (Italy)
Abstract: Latent space (LS) network models project the nodes of a network on a d-dimensional latent space to achieve dimensionality reduction of the network while preserving its relevant features. Inference is often carried out within a Markov chain Monte Carlo (MCMC) framework. Nonetheless, it is well-known that the computational time for this set of models increases quadratically with the number of nodes. The purpose is to build on the random-scan (RS) approach to propose an MCMC strategy that alleviates the computational burden for LS models while maintaining the benefits of a general-purpose technique. The novel sampling strategy effectively reduces the computational cost by a factor without severe consequences on the MCMC draws. Some convergence properties of the MCMC procedure are provided, and it is shown via simulation that this RS approach performs better than the standard RS in terms of mixing. Finally, the sampler is applied to a multi-layer temporal LS model, and it is shown how the adaptive strategy may be beneficial to empirical applications.
