نتایج جستجو برای: latent data
تعداد نتایج: 2449036 فیلتر نتایج به سال:
Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspon...
conclusions fib-4 confirmed the best value for diagnosis of significant fibrosis. apri had a sub-optimal diagnosis accuracy for significant fibrosis. lsm showed the most balance diagnosis value for cirrhosis with the highest sensitivity and moderate specificity. patients and methods a total of 544 patients with chb were assessed for fibrosis stage by four noninvasive tests containing liver stif...
Introducion: Few studies to date have shown the adverse effects of prolonged latent phase. Related factors, maternal and fetal outcomes of prolonged latent phase were studied in order to prevent these effects. Methods: In a cross-sectional study, 224 women were assessed at Shohada hospital by using questionnaire, examination and follow up to calculate the latent phase duration up to 3cm dilata...
Detecting and tracking latent factors from temporal data is an important task. Most existing algorithms for latent topic detection such as Nonnegative Matrix Factorization (NMF) have been designed for static data. These algorithms are unable to capture the dynamic nature of temporally changing data streams. In this paper, we put forward an online NMF (ONMF) algorithm to detect latent factors an...
In this contribution, we propose a generic online (also sometimes called adaptive or recursive) version of the Expectation-Maximisation (EM) algorithm applicable to latent variable models of independent observations. Compared to the algorithm of Titterington (1984), this approach is more directly connected to the usual EM algorithm and does not rely on integration with respect to the complete d...
We consider the problem of regression on multivariate count data and present a Gibbs sampler for a latent feature regression model suitable for both underand overdispersed response variables. The model learns countvalued latent features conditional on arbitrary covariates, modeling them as negative binomial variables, and maps them into the dependent count-valued observations using a Dirichlet-...
We describe methods to fit structured latent growth curves to data from MZ and DZ twins. The well-known Gompertz, logistic and exponential curves may be written as a function of three components - asymptote, initial value, and rate of change. These components are allowed to vary and covary within individuals in a structured latent growth model. Such models are highly economical, requiring a sma...
In this paper we address the problem of modeling relational data, which appear in many applications such as social network analysis, recommender systems and bioinformatics. Previous studies either consider latent feature based models but disregarding local structure in the network, or focus exclusively on capturing local structure of objects based on latent blockmodels without coupling with lat...
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