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Bayesian Inference, Metropolis-Langevin and Hamiltonian in Riemann manifolds.

Grant number: 15/00627-9
Support type:Scholarships abroad - Research
Effective date (Start): August 01, 2015
Effective date (End): July 31, 2016
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
Principal researcher:Ricardo Sandes Ehlers
Grantee:Ricardo Sandes Ehlers
Host: Nial Friel
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: University College Dublin, Ireland  

Abstract

In this project, recently proposed computationally intensive methods which explore ideas of Riemann geometry in the parameter space will be studied and applied in statistical models under the Bayesian approach. These methods seek to adapt to the local structure of a target distribution which can potentially turn Markov chain Monte Carlo methods (MCMC) more efficient. The computational costs involved may be high and therefore the cost-benefit must be investigated through simulation studies and real data analyses.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
HARTMANN, MARCELO; EHLERS, RICARDO S. Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. 46, n. 7, p. 5285-5302, 2017. Web of Science Citations: 1.
ZEVALLOS, MAURICIO; GASCO, LORETTA; EHLERS, RICARDO. Riemann manifold Langevin methods on stochastic volatility estimation. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v. 46, n. 10, p. 7942-7956, 2017. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.