Bayesian Analysis

On the Prior and Posterior Distributions Used in Graphical Modelling

Marco Scutari

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Graphical model learning and inference are often performed using Bayesian techniques. In particular, learning is usually performed in two separate steps. First, the graph structure is learned from the data; then the parameters of the model are estimated conditional on that graph structure. While the probability distributions involved in this second step have been studied in depth, the ones used in the first step have not been explored in as much detail.

In this paper, we will study the prior and posterior distributions defined over the space of the graph structures for the purpose of learning the structure of a graphical model. In particular, we will provide a characterisation of the behaviour of those distributions as a function of the possible edges of the graph. We will then use the properties resulting from this characterisation to define measures of structural variability for both Bayesian and Markov networks, and we will point out some of their possible applications.

Article information

Bayesian Anal. Volume 8, Number 3 (2013), 505-532.

First available in Project Euclid: 9 September 2013

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Zentralblatt MATH identifier

Markov Networks Bayesian Networks Random Graphs Structure Learning Multivariate Discrete Distributions


Scutari, Marco. On the Prior and Posterior Distributions Used in Graphical Modelling. Bayesian Anal. 8 (2013), no. 3, 505--532. doi:10.1214/13-BA819.

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See also

  • Related item: Adrian Dobra (2013). Comment on Article by Scutari. Bayesian Anal. Vol. 8, Iss. 3, 533–538.
  • Related item: Christine B. Peterson, Francesco C. Stingo (2013). Comment on Article by Scutari. Bayesian Anal. Vol. 8, Iss. 3, 539–542.
  • Related item: Hao Wang (2013). Comment on Article by Scutari. Bayesian Anal. Vol. 8, Iss. 3, 543–548.
  • Related item: Marco Scutari (2013). Rejoinder. Bayesian Anal. Vol. 8, Iss. 3, 549–552.