.png){kind=icon}
Return
Discrete Latent Variable Models
.png){kind=icon}
OA
AI
DOI：10.1146/annurev-statistics-040220-091910.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation-maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications available in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested.
Keywords:
data augmentation
expectation-maximization algorithm
finite mixture model
hidden Markov model
latent class model
model selection
stochastic block model
Journal
IF:
8.7
Papers:
211
Citations:
2.4K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave
View more