Current Trends in Bayesian Methodology with Applications
Priors on Hypergraphical Models via Simplicial Complexes
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, Chapter
Lunagómez, S; Mukherjee, S; Wolpert, R
January 1, 2015
It is common to model the joint probability distribution of a family of n random variables {X1, . . . , Xn} in two stages: First to specify the conditional dependence structure of the distribution, then to specify details of the conditional distributions of the variables within that structure [3, 7]. The structure may be summarized in a variety of ways in the form of a graph G = (V , E) whose vertices V = {1, ..., n} index the variables {Xi} and whose edges E ? V V in some way encode conditional dependence.
Duke Scholars
DOI
ISBN
9781482235111
Publication Date
January 1, 2015
Start / End Page
391 / 414
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Lunagómez, S., Mukherjee, S., & Wolpert, R. (2015). Priors on Hypergraphical Models via Simplicial Complexes. In Current Trends in Bayesian Methodology with Applications (pp. 391–414). https://doi.org/10.1201/b18502-26
Lunagómez, S., S. Mukherjee, and R. Wolpert. “Priors on Hypergraphical Models via Simplicial Complexes.” In Current Trends in Bayesian Methodology with Applications, 391–414, 2015. https://doi.org/10.1201/b18502-26.
Lunagómez S, Mukherjee S, Wolpert R. Priors on Hypergraphical Models via Simplicial Complexes. In: Current Trends in Bayesian Methodology with Applications. 2015. p. 391–414.
Lunagómez, S., et al. “Priors on Hypergraphical Models via Simplicial Complexes.” Current Trends in Bayesian Methodology with Applications, 2015, pp. 391–414. Scopus, doi:10.1201/b18502-26.
Lunagómez S, Mukherjee S, Wolpert R. Priors on Hypergraphical Models via Simplicial Complexes. Current Trends in Bayesian Methodology with Applications. 2015. p. 391–414.
DOI
ISBN
9781482235111
Publication Date
January 1, 2015
Start / End Page
391 / 414