We present a latent variable structured prediction model, called the Latent Left-linking Model (L3M), for discriminative supervised clustering of items that follow a streaming order. LM admits efficient inference and we present a learning framework for LM that smoothly interpolates between latent structural SVMs and hidden variable CRFs. We present a fast stochastic gradientbased learning techn...

A powerful approach to probabilistic modelling involves supplementing a set of observed variables with additional latent, or hidden, variables. By defining a joint distribution over visible and latent variables, the corresponding distribution of the observed variables is then obtained by marginalization. This allows relatively complex distributions to be expressed in terms of more tractable joi...

Consider a set of multiple, multimodal sensors capturing a complex system or a physical phenomenon of interest. Our primary goal is to distinguish the underlying sources of variability manifested in the measured data. The first step in our analysis is to find the common source of variability present in all sensor measurements. We base our work on a recent paper, which tackles this problem with ...

in this study, the effect of environmental concern, attitudes and behaviors of the university students on the environmentally conscious purchasing behavior was investigated with the help of structural equation model (sem). sem is an effective data analysis tool that expresses the complicated causative relations between the latent variables. in this study, we collected the data from 400 univers...

Certain causal models involving unmea­ sured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality con­ straints on the observed distribution. This paper derives a general formula for such in­ equality constraints as induced by instru­ mental variables, that is, exogenous vari­ ables that directly affect some variables but not all. With th...

Unobservable variables in econometrics are represented in one of three ways: by variables contaminated by measurement errors, by proxy variables, or by various manifest indicators and/or causes. This paper contains a discussion of models involving each of these representations, and highlights certain interesting implications that have been ‘insufficiently emphasized or completely unrecognized i...

Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified. When one analyzes data using such a model, one is looking for one way to cluster data that is jointly defined by all the attributes. In other words, one performs unidimensional clustering. This ...

The application of latent/hidden variable Dynamic Bayesian Networks is constrained by the complexity of marginalising over latent variables. For this reason either small latent dimensions or Gaussian latent conditional tables linearly dependent on past states are typically considered in order that inference is tractable. We suggest an alternative approach in which the latent variables are model...