Latent variables and space-time models for environmental problems

Latent Variables, Causal Models, and Overidentifying Constraints

Latent Variables, Causal Models and Overidentieying Constraints

When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model specification developed more fully in our book Discovering Cuud Structure, and illustrate its applicat...

Learning Non-Stationary Space-Time Models for Environmental Monitoring

One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both space and time and it is imperative to develop a deeper understanding of techniques that can model space-time dynamics accurately. In this paper we propose NOSTILL-GP NOn-stationary Space TIme variable Latent Length sca...

Concomitant Variables in Latent Change Models

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 ...

Shared Gaussian Process Latent Variables Models

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...