Graphical models for categorical data / Alberto Roverato.
Series: SemStat elementsPublication details: Cambridge : Cambridge University Press, 2017.Description: vii, 152 pages : illustrations ; 23 cmISBN:- 9781108404969
- 000SA.061 23 R873
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.061 R873 (Browse shelf(Opens below)) | Available | 138426 |
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| 000SA.06 P769 Model-free prediction and regression : | 000SA.06 Sa163 Statistical inference for models with multivariate t-distributed errors / | 000SA.06 T136 Learning regression analysis by simulation / | 000SA.061 R873 Graphical models for categorical data / | 000SA.062 Ag277 Foundations of linear and generalized linear models / | 000SA.062 C435 Some nonparametric hybrid predictive models: asymptotic properties and applications/ | 000SA.062 C554 Plane answers to complex questions |
Includes bibliographical references.
1. Introduction --
2. Conditional Independence and Cross-product Ratios --
3. Mobius Inversion --
4. Undirected Graph Models --
5. Bidirected Graph Models --
6. Directed Acyclic and Regression Graph Models.
For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.
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