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On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models (2017)
Journal Article
Karagiannis, G., & Lin, G. (2017). On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. Journal of Computational Physics, 342, 139-160. https://doi.org/10.1016/j.jcp.2017.04.003

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesi... Read More about On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models.

Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation (2016)
Journal Article
Karagiannis, G., Konomi, B., Lin, G., & Liang, F. (2016). Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation. Statistics and Computing, 27(4), 927-945. https://doi.org/10.1007/s11222-016-9663-0

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte... Read More about Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation.

Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions (2015)
Journal Article
Zhang, B., Konomi, B., Sang, H., Karagiannis, G., & Lin, G. (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics, 300, 623-642. https://doi.org/10.1016/j.jcp.2015.08.006

Gaussian process emulator with separable covariance function has been utilized extensively in modeling large computer model outputs. The assumption of separability imposes constraints on the emulator and may negatively affect its performance in some... Read More about Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions.

A Bayesian mixed shrinkage prior procedure for spatial–stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs (2015)
Journal Article
Karagiannis, G., Konomi, B., & Lin, G. (2015). A Bayesian mixed shrinkage prior procedure for spatial–stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs. Journal of Computational Physics, 284, 528-546. https://doi.org/10.1016/j.jcp.2014.12.034

We propose a new fully Bayesian method to efficiently obtain the spectral representation of a spatial random field, which can conduct spatial–stochastic basis selection and evaluation of generalized Polynomial Chaos (gPC) expansions when the number o... Read More about A Bayesian mixed shrinkage prior procedure for spatial–stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs.

On the Bayesian treed multivariate Gaussian process with linear model of coregionalization (2014)
Journal Article
Konomi, B., Karagiannis, G., & Lin, G. (2015). On the Bayesian treed multivariate Gaussian process with linear model of coregionalization. Journal of Statistical Planning and Inference, 157-158, 1-15. https://doi.org/10.1016/j.jspi.2014.08.010

The Bayesian treed multivariate Gaussian process (BTMGP) and Bayesian treed Gaussian process (BTGP) provide straightforward mechanisms for emulating non-stationary multivariate computer codes that alleviate computational demands by fitting models loc... Read More about On the Bayesian treed multivariate Gaussian process with linear model of coregionalization.

Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit (2014)
Journal Article
Konomi, B., Karagiannis, G., Sarkar, A., Sun, X., & Lin, G. (2014). Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit. Technometrics, 56(2), 145-158. https://doi.org/10.1080/00401706.2013.879078

Computer experiments are widely used in scientific research to study and predict the behavior of complex systems, which often have responses consisting of a set of nonstationary outputs. The computational cost of simulations at high resolution often... Read More about Bayesian treed multivariate Gaussian process with adaptive design: Application to a carbon capture unit.

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.