About Pedro Andres Vilanova Guerra Pedro Andres Vilanova Guerra Postdoctoral Research Fellow, Stochastic Numerics Research Group Pedro Andres Vilanova Guerra 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, Pedro Andres obtained a PhD degree in Applied Mathematics from KAUST. Research Interests Pedro Andres' research interests included Error control and adaptive algorithms for Markovian processes and Statistical inference for Markovian processes. Selected Publications A. Moraes, F. Ruggeri, R. Tempone, and P. Vilanova. Multiscale modeling of wear degradation in cylinder liners. Multiscale 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.
Related Sites Stochastic Numerics Research Group (STOCHNUM) Applied Mathematics and Computational Science (AMCS) Related Content Articles 11 Projects 3 Related Links Publications on ResearchGate LinkedIn Profile
