Stratified stochastic variational inference for high-dimensional network factor model
| Autor: | Emanuele Aliverti, Massimiliano Russo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Bayesian inference Sparsity Stochastic optimization Variational methods Computer science Inference High dimensional Space (mathematics) Statistics - Computation Methodology (stat.ME) Factor (programming language) Discrete Mathematics and Combinatorics Statistics - Methodology Computation (stat.CO) computer.programming_language Markov chain Statistics Probability and Uncertainty Settore SECS-S/01 - Statistica computer Algorithm |
| Popis: | There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov Chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. Comment: fixed compilation issues |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|