A latent Gaussian Markov random-field model for spatiotemporal rainfall disaggregation

A latent Gaussian Markov random field model for spatio- temporal rainfall disaggregation

Rainfall data are often collected at coarser spatial scales than required for input into hydrology and agricultural models. We therefore describe a spatio-temporal model which allows multiple imputation of rainfall at fine spatial resolutions, with a realistic dependence structure in both space and time and with the total rainfall at the coarse scale consistent with that observed. The method in...

Gibbs Sampling for the Probit Regression Model with Gaussian Markov Random Field Latent Variables

We consider a binary probit model where the latent variable follow a Gaussian Markov Random Field (GMRF). Our main objective is to derive an efficient Gibbs sampler for the above model. For this purpose, we first review two Gibbs samplers available for the classical probit model with one latent variable. We find that the joint update of variables increases the rate of convergence. We use these ...

Gaussian Process Latent Random Field

The Gaussian process latent variable model (GPLVM) is an unsupervised probabilistic model for nonlinear dimensionality reduction. A supervised extension, called discriminative GPLVM (DGPLVM), incorporates supervisory information into GPLVM to enhance the classification performance. However, its limitation of the latent space dimensionality to at most C − 1 (C is the number of classes) leads to ...

Multivariate latent Gaussian random field mixture models

Generating synthetic rainfall using a disaggregation model

Synthetic rainfall data has a large number of uses. Specifically, the behaviour of a reservoir under most scenarios can be best understood when evaluated using a large number of synthetic inputs. These synthetic values are all equally probable to occur but many of them may not have appeared in the historical record. Stochastic weather generators produce these synthetic data that were statistica...