Building hyper Dirichlet processes for graphical models
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Publication:1951978
DOI10.1214/08-EJS269zbMath1326.62012arXiv0807.3410MaRDI QIDQ1951978
Publication date: 27 May 2013
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0807.3410
nonparametric priorcovariance selectionhyper Markov lawdecomposable graphical distributionstick-breaking measure
Stochastic processes (60G99) Nonparametric inference (62G99) Graphical methods in statistics (62A09)
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- Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
- Gaussian Markov distributions over finite graphs
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- On posterior consistency of survival models
- Sparse graphical models for exploring gene expression data
- Theory of statistics
- Logit models and logistic regressions for social networks. III: Valued relations
- Asymptotic Bayesian structure learning using graph supports for Gaussian graphical models
- A Bayesian analysis of some nonparametric problems
- Wishart distributions for decomposable graphs
- Corrigenda: Extrema of Quadratic Forms with Applications to Statistics
- An Exponential Family of Probability Distributions for Directed Graphs
- Nonparametric Bayesian Estimation of Survival Curves from Incomplete Observations
- The Isserlis matrix and its application to non-decomposable graphical Gaussian models
- Analysing the Interevent Time Distribution to Identify Seismicity Phases: A Bayesian Nonparametric Approach to the Multiple-Changepoint Problem
- Bayesian Density Estimation and Inference Using Mixtures
- Decomposable graphical Gaussian model determination
- Simulation of hyper-inverse Wishart distributions in graphical models
- Order-Based Dependent Dirichlet Processes
