Rainfall Modelling Using a Latent Gaussian Variable
نویسندگان
چکیده
A monotonic transformation is applied to hourly rainfall data to achieve marginal normality. This deenes a latent Gaussian variable, with zero rainfall corresponding to censored values below a threshold. Autocor-relations of the latent v ariable are estimated by maximum likelihood. The goodness of t of the model to Edinburgh rainfall data is comparable with that of existing point process models. Gibbs sampling is used to disaggre-gate daily rainfall data, to generate typical hourly data conditional on daily totals.
منابع مشابه
Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...
متن کاملSpeech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model
In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Both data are processed separately and subsequently coupled together to yield a shared latent space. This method allows coarticulation to be modelled by having a dynamical model on the latent space. Synthesis of novel animation is...
متن کاملStochastic Variational Inference for Gaussian Process Latent Variable Models using Back Constraints
Gaussian process latent variable models (GPLVMs) are a probabilistic approach to modelling data that employs Gaussian process mapping from latent variables to observations. This paper revisits a recently proposed variational inference technique for GPLVMs and methodologically analyses the optimality and different parameterisations of the variational approximation. We investigate a structured va...
متن کاملA Bayesian Prediction using the Elliptical and the Skew Gaussian Processes
A Bayesian Prediction using the Elliptical Processes (EP) and the Skew Gaussian Processes (SGP) is proposed, motivated by a Bayesian model for heavy, light tailed or skewed real data. We define weak third order stationary for the Skew Gaussian Processes. Sometimes the family of distributions have dimensional coherency (consistency) property which is important for prediction. We use a Markov Cha...
متن کاملKernel Topic Models
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents’ mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling te...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1997