Modeling the dynamics of social networks using Bayesian hierarchical blockmodels
From MaRDI portal
Publication:4969839
DOI10.1002/sam.10150OpenAlexW2070987660MaRDI QIDQ4969839
Publication date: 14 October 2020
Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/sam.10150
Related Items (2)
High-dimensional Bayesian network classification with network global-local shrinkage priors ⋮ Neuronal Spike Train Analysis Using Gaussian Process Models
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
- Ferguson distributions via Polya urn schemes
- Inference from iterative simulation using multiple sequences
- Exchangeable and partially exchangeable random partitions
- An exploration of aspects of Bayesian multiple testing
- A Bayesian analysis of some nonparametric problems
- Particle learning for general mixtures
- On-Line Learning for the Infinite Hidden Markov Model
- Mixed membership stochastic blockmodels
- Hierarchical Dirichlet Processes
- Sampling the Dirichlet Mixture Model with Slices
- Mixtures of g Priors for Bayesian Variable Selection
- Markov Graphs
- An Exponential Family of Probability Distributions for Directed Graphs
- Latent Space Approaches to Social Network Analysis
- Stochastic Blockmodels for Directed Graphs
- Bayesian Density Estimation and Inference Using Mixtures
This page was built for publication: Modeling the dynamics of social networks using Bayesian hierarchical blockmodels
