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Key Features:
A general framework for learning sparse graphical models with conditional independence tests
Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data
Unified treatments for data integration, network comparison, and covariate adjustment
Unified treatments for missing data and heterogeneous data
Efficient methods for joint estimation of multiple graphical models
Effective methods of high-dimensional variable selection
Effective methods of high-dimensional inference
Key Features:
A general framework for learning sparse graphical models with conditional independence tests
Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data
Unified treatments for data integration, network comparison, and covariate adjustment
Unified treatments for missing data and heterogeneous data
Efficient methods for joint estimation of multiple graphical models
Effective methods of high-dimensional variable selection
Effective methods of high-dimensional inference
Dr. Faming Liang is Distinguished Professor of Statistics, Purdue University. Prior joining Purdue University in 2017, he held regular faculty positions in the Department of Biostatistics, University of Florida and Department of Statistics, Texas A&M University. Dr. Liang obtained his PhD degree from the Chinese University of Hong Kong in 1997. Dr. Liang is ASA fellow, IMS fellow, and elected member of International Statistical Association. Dr. Liang is also a winner of Youden Prize 2017. Dr. Liang has served as co-editor for Journal of Computational and Graphical Statistics, associate editor for multiple statistical journals, including Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Technometrics, Bayesian Analysis, and Biometrics, and editorial board member for Nature Scientific Report. Dr. Liang has published two books and over 130 journal/conference papers, which involve a variety of research fields such as Markov chain Monte Carlo, machine learning, bioinformatics, high-dimensional statistics, and big data computing.
Dr. Bochao Jia is research scientist at Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, U.S.A. Dr. Jia obtained his PhD degree from University of Florida in 2018. Dr. Jia has published quite a few papers on sparse graphical modelling.
1. Introduction to Sparse Graphical Models 2. Gaussian Graphical Models 3. Gaussian Graphical Modeling with Missing Data 4. Gaussian Graphical Modeling for Heterogeneous Data 5. Poisson Graphical Models 6. Mixed Graphical Models 7. Joint Estimation of Multiple Graphical Models 8. Nonlinear and Non-Gaussian Graphical Models 9. High-Dimensional Inference with the Aid of Sparse Graphical Modeling 10. Appendix
| Erscheinungsjahr: | 2023 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Importe, Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: | Einband - fest (Hardcover) |
| ISBN-13: | 9780367183738 |
| ISBN-10: | 0367183730 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Liang, Faming
Jia, Bochao |
| Hersteller: | Chapman and Hall/CRC |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 240 x 161 x 13 mm |
| Von/Mit: | Faming Liang (u. a.) |
| Erscheinungsdatum: | 02.08.2023 |
| Gewicht: | 0,399 kg |