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Generalized Measurement Models

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Title:
Generalized Measurement Models
Author: Silva, Ricardo ; Scheines, Richard
Subjects: ALGORITHMS ; ARTIFICIAL INTELLIGENCE ; BAYES THEOREM ; CAUSAL GRAPHS ; CAUSALITY DISCOVERY ; CLUSTERING ; CONSISTENCY ; COVARIANCE ; COVARIANCE MATRIX ; Cybernetics ; DATA MINING ; FACTOR ANALYSIS ; GRAPHICAL MODELS ; GRAPHS ; HIDDEN VARIABLES ; INDICATORS ; LATENT VARIABLE GRAPHS ; LATENT VARIABLES ; LEARNING MACHINES ; LEARNING MEASUREMENT MODELS ; MACHINE LEARNING ALGORITHMS ; MARKOV PROCESSES ; MATRICES(MATHEMATICS) ; MEASUREMENT ; NEURAL NETS ; NODES ; OBSERVATION ; OBSERVED VARIABLES ; PARAMETERS ; RANDOM VARIABLES ; STATISTICAL LEARNING ; Statistics and Probability ; STRUCTURAL EQUATION MODELS ; TETRAD CONSTRAINTS ; TETRAD EQUIVALENCE CLASS ; UNCERTAINTY
Description: Given a set of random variables, it is often the case that their associations can be explained by hidden common causes. The authors present a set of well-defined assumptions and a provably correct algorithm that allow them to identify some of those hidden common causes. The assumptions are fairly general and sometimes weaker than those used in practice by econometricians, psychometricians, social scientists, and by experts in many other fields in which latent variable models are important and tools such as factor analysis are applicable. The goal is automated knowledge discovery: identifying latent variables that can be used across different applications and causal models and that provide new insights into a data generating process. Their approach is evaluated through simulations and three real-world cases. Sponsored in part by NASA-NRA-A2-37143.
Creation Date: 2005
Language: English
Source: DTIC Technical Reports

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Generalized Measurement Models

Approved for public release; distribution is unlimited. This document is not available from DTIC in microfiche.

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