Latent variable models exist with continuous, categorical, or both types of latent variables. The role of latent variables is to account for systematic patterns in the observed responses. This article has two goals: (a) to establish whether, based on observed responses, it can be decided that an underlying latent variable is continuous or categorical, and (b) to quantify the effect of sample si...

Partial Least Squares Regression (PLSR) is a method for constructing predictive models when the variables are many and highly collinear. Its goal is to predict a set of response variables from a set of predictor variables. This prediction is achieved by extracting a set of orthogonal factors called latent variables from the predictor variables. This study investigated the performances of model ...

Latent variable techniques are helpful to reduce high-dimensional time series to a few relevant variables that are easier to model and analyze. An inherent problem is the identifiability of the model and the interpretation of the latent variables. We apply graphical models to find the essential relations in the data and to deduce suitable assumptions leading to meaningful latent variables.

We present a formalization of first-order predicate calculus with equality which, unlike traditional systems with axiom schemata or substitution rules, is finitely axiomatized in the sense that each step in a formal proof admits only finitely many choices. This formalization is primarily based on the inference rule of condensed detachment of C. A. Meredith. The usual primitive notions of free v...

in this paper, we introduce a new kind of order, cesaro supermodular order, which includes supermodular order and stochastic order. for this new order, we show that it almost fulfils all desirable properties of a multivariate positive dependence order that have been proposed by joe (1997). also, we obtain some relations between it with other orders. finally, we consider different issues related...

Abstract: The purpose of this work is to describe a unified, and indeed simple, mechanism for non-parametric Bayesian analysis, construction and generative sampling of a large class of latent feature models which one can describe as generalized notions of Indian Buffet Processes (IBP). This is done via the Poisson Process Calculus as it now relates to latent feature models. The IBP, first arisi...

A basic tenet in modeling preference behavior is that individuals differ in the ways they perceive and evaluate choice options. Latent-class analysis provides a parsimonious and flexible approach to represent these taste differences. This method decomposes a heterogeneous population of decision-makers into several homogeneous classes or subpopulations. Each decision-maker is assigned to one of ...

We argue that many models for multivariate longitudinal and cross-sectional data analysis have a common ancestry. They all are based on the qualitative idea that if we knew the actual state of the world, the relations between the observed quantities would be truly simple. This is shown to lead directly to factor analysis, IRT, state space models, mixture densities, latent Markov chains, MIMIC, ...

A fundamental task is machine learning is modeling the relationship between different observation spaces. Dimensionality reduction is the task reducing the number of dimensions in a parameterization of a data-set. In this thesis we are interested in the cross-road between these two tasks: shared dimensionality reduction. Shared dimensionality reduction aims to represent multiple observation spa...

In this paper we address the problem of classifying objects (e.g. person or car) and actions (e.g. hugging or eating) [2]. The more successful methods are based on a uniform pyramidal representation (SPM) built on a visual word vocabulary [1]. In this paper, we augment the classification by adding more flexible spatial information. This will be formulated more generally as inferring additional ...