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Non-parametric Bayesian inference applied on estimation and forecasting of time series

Grant number: 12/03000-9
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): July 01, 2012
Effective date (End): December 31, 2012
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Fernando Antonio Moala
Grantee:Marcelo Hartmann
Home Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil

Abstract

Gaussian processes jointly with bayesian methods are applied on the estimation of functions, thats it, there is no supposition of a unique parametric functional form underlying the data. In this approach of non-parametric inference, the density function or the function of interest can be estimated without the imposition of any restrictive supposition of its form. The data allow determine the estimate of the interested function rather than conditioning it to a given parametric class functions. Its also performed some application in time series models.