We show a close relationship between the Expectation Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We identify analytic conditions under which EM exhibits Newton-like behavior, and conditions under which it possesses poor, first-order convergence. Based on this analysis, we propose two novel algorithms for maximum likelihood...

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use con...

Treating Likert scale data as continuous outcomes in confirmatory factor analysis violates the assumption of multivariate normality. Given certain requirements pertaining to the number of categories, skewness, size of the factor loadings, etc., it seems nevertheless possible to recover true parameter values if the data stem from a single homogenous population. It is shown in a multi-group and a...

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 ...

the present study attempts to analyze the evaluation of the central district of ravansar township rural community from the performance of village administrations (dehyari) by using of structural equation modeling (sem). in order to collect needed data, a questionnaire was designed and completed via interviewing the sample population. the observed variables for economical, socio-cultural, servic...

Data often take the form of ordered categories. For instance, in education, test results are often reported as grades. Where a hierarchical structure exists for the data, multilevel modelling of such ordered categorisations can be carried out using macros in MLwiN. The ordered categorisation can be seen as the realisation of an unknown underlying latent variable. A link function is used to rela...

Many constructs developmental scientists study cannot be directly observed. In such cases, scales are created that reflect the construct of interest. Observed behaviors are taken as manifestations of an unobserved common cause. As crucial as measurement is to understanding many psychological phenomenon, it is perhaps even more important when the goal of research is to understand how a construct...

In previous work, we have developed a principled way of learning the causal structure of linear latent variable models (Silva et al., 2006). However, we have considered the case for models with pure measures only. Pure measures are observed variables that measure no more than one latent variable. This paper presents theoretical extensions that justify the selection of some types of impure measu...