About Alvaro Moraes Alvaro Moraes Postdoctoral Research Fellow, Stochastic Numerics Research Group uncertainty quantification Alvaro Moraes worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Prior to that, Alvaro obtained a Ph.D. degree in Applied Mathematics from KAUST. Research Interests Alvaro's research interests included Uncertainty Quantification, Multiscale Modeling of Epidemic Diseases, and their Statistical Analysis and Simulation and Inference for Pure Jump Processes. Selected Publications A. Moraes, F. Ruggeri, R. Tempone and P. Vilanova, " Multiscale Modeling of Wear Degradation in Cylinder Projects Related Projects 2016 The forward-reverse algorithm for stochastic reaction networks with applications to statistical inference Thu, Feb 18 - Sun, Jul 10 2016 In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [Annals of Applied Probability, 24(5):1994--2032, October 2014] to the context of stochastic reaction networks (SRNs). It makes the approximation of expected values of functionals of bridges for this type of process computationally feasible. 2014 Multilevel Approximation of Stochastic Reaction Networks Thu, Apr 24 2014 - Tue, May 10 2016 Stochastic Reaction Networks is a class of Markovian pure jump processes that model a wide range of phenomena, including chemical reactions at the molecular level, dynamics of wireless communication networks, and the spread of epidemic diseases in small populations. Multiscale Inference for Pure Jump Processes Wed, Jan 1 - Mon, Dec 1 2014 We aim to use a multiscale sequential Bayesian inference approach. It is multiscale because we have a continuous-time discrete-state pure jump process base microscopic model and then two levels of approximation.
The forward-reverse algorithm for stochastic reaction networks with applications to statistical inference Thu, Feb 18 - Sun, Jul 10 2016 In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [Annals of Applied Probability, 24(5):1994--2032, October 2014] to the context of stochastic reaction networks (SRNs). It makes the approximation of expected values of functionals of bridges for this type of process computationally feasible.
Multilevel Approximation of Stochastic Reaction Networks Thu, Apr 24 2014 - Tue, May 10 2016 Stochastic Reaction Networks is a class of Markovian pure jump processes that model a wide range of phenomena, including chemical reactions at the molecular level, dynamics of wireless communication networks, and the spread of epidemic diseases in small populations.
Multiscale Inference for Pure Jump Processes Wed, Jan 1 - Mon, Dec 1 2014 We aim to use a multiscale sequential Bayesian inference approach. It is multiscale because we have a continuous-time discrete-state pure jump process base microscopic model and then two levels of approximation.
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