A BAYESIAN TREATMENT OF LATENT VARIABLES IN SAMPLE SURVEYS
نویسندگان
چکیده
منابع مشابه
Learning Linear Bayesian Networks with Latent Variables
This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established under a novel graphical constraint. The constraint concerns the expansion properties of the underlying directed acyclic graph (DAG) between observed and unobserved variables in the network, and it...
متن کاملA Method of Learning Latent Variables Dimensionality for Bayesian Networks
Latent variables often play an important role in improving the quality of the learned Bayesian networks and understanding the nature of interactions in the model. The dimensionality of latent variables has significant effect on the representation quality and complexity of the model. The maximum possible dimensionality of a latent variable is a Cartesian product of the state space of its Markov ...
متن کاملBayesian modeling of embryonic growth using latent variables.
In a growth model, individuals move progressively through a series of states in which each state is indicative of developmental status. Interest lies in estimating the rate of progression through each state while incorporating covariates that might affect the transition rates. We develop a Bayesian discrete-time multistate growth model for inference from cross-sectional data with unknown initia...
متن کاملModel Criticism of Bayesian Networks with Latent Variables
The application of Bayesian networks (BNs) to cognitive assessment and intelligent tutoring systems poses new challenges for model construction. When cognitive task analyses suggest constructing a BN with several latent variables, empirical model criticism of the latent structure becomes both critical and complex. This paper introduces a methodology for criticizing models both globally (a BN in...
متن کاملBayesian Semiparametric Structural Equation Models with Latent Variables
Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semipa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ETS Research Report Series
سال: 1986
ISSN: 2330-8516
DOI: 10.1002/j.2330-8516.1986.tb00155.x