Name
Title | ||
|---|---|---|
New d-separation identification results for learning continuous latent variable models |
Abstract | ||
|---|---|---|
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the d-separations that define the graphical structure. This paper describes new distribution-free techniques for identifying d-separations in continuous latent variable models when non-linear dependencies are allowed among hidden variables. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1102351.1102453 | ICML |
Keywords | Field | DocType |
considerable difficulty,important task,new d-separation identification result,conditional independence,new distribution-free technique,graphical structure,alternative method,latent variable,hidden variable,continuous latent variable model,graphical model,hidden variables,latent variable model | Structural equation modeling,Pattern recognition,Computer science,Latent variable model,Latent class model,Latent variable,Probabilistic latent semantic analysis,Artificial intelligence,Hidden variable theory,Graphical model,Local independence,Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-180-5 | 2 | 1.11 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Bezerra de Andrade e Silva | 1 | 109 | 24.56 |
| Richard Scheines | 2 | 256 | 37.19 |