All Outputs (2)

Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs (2013)
Journal Article
Karagiannis, G., & Lin, G. (2014). Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs. Journal of Computational Physics, 259, 114-134. https://doi.org/10.1016/j.jcp.2013.11.016

Generalized polynomial chaos (gPC) expansions allow us to represent the solution of a stochastic system using a series of polynomial chaos basis functions. The number of gPC terms increases dramatically as the dimension of the random input variables... Read More about Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs.

Annealed Importance Sampling Reversible Jump MCMC Algorithms (2013)
Journal Article
Karagiannis, G., & Andrieu, C. (2013). Annealed Importance Sampling Reversible Jump MCMC Algorithms. Journal of Computational and Graphical Statistics, 22(3), 623-648. https://doi.org/10.1080/10618600.2013.805651

We develop a methodology to efficiently implement the reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms of Green, applicable for example to model selection inference in a Bayesian framework, which builds on the “dragging fast variables” i... Read More about Annealed Importance Sampling Reversible Jump MCMC Algorithms.