Discovering latent covariance structures for multiple time series
Analyzing multivariate time series data is important to predict future events and changes of
complex systems in finance, manufacturing, and administrative decisions. The …
complex systems in finance, manufacturing, and administrative decisions. The …
A Pattern Discovery Approach to Multivariate Time Series Forecasting
Multivariate time series forecasting constitutes important functionality in cyber-physical
systems, whose prediction accuracy can be improved significantly by capturing temporal …
systems, whose prediction accuracy can be improved significantly by capturing temporal …
A fully natural gradient scheme for improving inference of the heterogeneous multioutput Gaussian process model
JJ Giraldo, MA Alvarez - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
A recent novel extension of multioutput Gaussian processes (GPs) handles heterogeneous
outputs, assuming that each output has its own likelihood function. It uses a vector-valued …
outputs, assuming that each output has its own likelihood function. It uses a vector-valued …
Variational Optimisation for Non-conjugate Likelihood Gaussian Process Models
JJ Giraldo Gutierrez - 2021 - etheses.whiterose.ac.uk
In this thesis we address the problems associated to non-conjugate likelihood Gaussian
process models, ie, probabilistic models where the likelihood function and the Gaussian …
process models, ie, probabilistic models where the likelihood function and the Gaussian …
[PDF][PDF] Uma abordagem computacional para predição de mortalidade em UTIs baseada em agrupamento de Processos Gaussianos
RG Caixeta - 2016 - ww2.inf.ufg.br
Resumo Caixeta, Rommell Guimarães. Uma Abordagem Computacional para Predição de
Mortalidade em UTIs Baseada em Agrupamento de Processos Gaussianos. Goiânia, 2016 …
Mortalidade em UTIs Baseada em Agrupamento de Processos Gaussianos. Goiânia, 2016 …