In high throughput settings we inspect a great many candidate variables (e.g. genes) searching for associations with a primary variable (e.g. a phenotype). High throughput hypothesis testing can be made difficult by the presence of systemic effects and other latent variables. It is well known that those variables alter the level of tests and induce correlations between tests. It is less well kn...

There is currently considerable interest in developing general non-linear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying`causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortunately , to train such models generally requires large computati...

This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established under a novel graphical constraint. The constraint concerns the expansion properties of the underlying directed acyclic graph (DAG) between observed and unobserved variables in the network, and it...

We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use...

In an effort to better understand the complex courtship behaviour of pigeons, we have built a model learned from motion capture data. We employ a Conditional Restricted Boltzmann Machine with binary latent features and real-valued visible units. The units are conditioned on information from previous time steps in a sequence to learn long-term effects and infer current features. We validate a tr...

We consider classical and Bayesian estimation procedures implemented by means of a set of conditional moment conditions that depend on latent variables. The latent variables evolve according to a Markovian transition density. Two main classes of applications are: 1) GMM estimation with time-varying parameters; and 2) estimation of nonlinear Dynamic Stochastic General Equilibrium (DSGE) models. ...

We consider behaviors in which we distinguish two types of variables, mani[est variables, the variables that are of interest to the user and latent variables, the variables that are introduced to obtain a first representation. The problem is to find a representation of th,: manifest behavior, that is, we want to eliminate the latent variables. If the original behavior can be represented by line...

In this paper, we propose a stratified topic model (STM). Instead of directly modeling and inferring flat topics or hierarchically structured topics, we use the stratified relationships in topic hierarchies to regularize the flat topics. The topic structures are captured by a hierarchical clustering method and play as constraints during the learning process. We propose two theoretically sound a...