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Published Material

S.Agapiou, J.M.Bardsley, O.Papaspiliopoulos, A.M.Stuart, Analysis of the Gibbs sampler for hierarchical inverse problems. SIAM JUQ, 2 (2014) 514-544.
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S.Agapiou, S.Larsson, A.M. Stuart, Posterior consistency of the Bayesian approach to linear ill-posed inverse problems. Stochastic Processes and Applications, 123/10 (2013) 3828-3860.
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S.Agapiou, A.M.Stuart, Y-X.Zhang, Bayesian posterior contraction rates for linear severely ill-posed inverse problems. Journal of Inverse and Ill-Posed Problems, 22(2014), 297-321.
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H.Bazargan, M.A.Christie, 'Bayesian Model Selection for Complex Geological Structures Using Polynomial Chaos Proxy' Computational Geosciences (2017).
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H.Bazargan, M.A.Christie, A.H.Elsheikh, M.Ahmadi, Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion, Advances in Water Resources, vol 86, no. Part B (2015) pp. 385–399. DOI: 10.1016/j.advwatres.2015.09.009
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A.Beskos, M.Girolami, S.Lan, P.E.Farrell, A.M.Stuart, Geometric MCMC for Infinite-Dimensional Inverse Problems. Journal of Computational Physics Volume 335, 15 April 2017, Pages 327-351.
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A.Beskos, A.Jasra, E.A.Muzaffer, A.M.Stuart, Sequential Monte Carlo methods for Bayesian elliptic inverse problems. Stat. Comp. 25/4 (2015) 727-737.
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A.Beskos, N.Pillai, G.Roberts, J.-M.Sanz-Serna, A.M.Stuart, Optimal tuning of the hybrid Monte Carlo algorithm. Bernoulli 19(2013), 1501-1534.
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M.Betancourt, S.Byrne, S.Livingstone, M.Girolami (2016) The Geometric Foundations of Hamiltonian Monte Carlo, Bernoulli, 23(4A), 2257 - 2298, 2017. DOI: 10.3150/16-BEJ810.
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D.Bloemker, K.J.H.Law, A.M.Stuart, K.Zygalalkis, Accuracy and stability of the continuous-time 3DVAR filter for the Navier-Stokes equation. Nonlinearity 26(2013), 2193-2219.
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C.E.A.Brett, K.F.Lam, K.J.H.Law, D.S.McCormick, M.R.Scott, A.M.Stuart, Accuracy and stability of filters for dissipative PDEs. Physica D 245(2013) 34-45
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F.-X.Briol, C.Oates, M.Girolami, M.A.Osborne, (2015). Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.Advances In Neural Information Processing Systems (NIPS) 2015.
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T.Bui, M.Girolami, Solving Large-Scale PDE-constrained Bayesian Inverse problems with Riemann Manifold Hamiltonian Monte Carlo. Inverse Problems, 30 (2104) 114014.
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O.A.Chkrebtii, D.A.Campbell, B.Calderhead, M.A.Girolami. Bayesian Solution Uncertainty Quantification for Differential Equations. Bayesian Analysis, Vol. 11, Number. 4, Pages 1239-1267. with Discussion, 2016.
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P.R.Conrad, M.Girolami, S.Sarkka, A.M.Stuart, K.C.Zygalakis, Statistical analysis of differential equations: introducing probability measures on numerical solutions. Statistics and Computing (2016).
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P. Conrad, MAG, S.Sarkka, A.M.Stuart, K.Zygalkis. Probability Measures for Numerical Solutions of Differential Equations, Statistics and Computing, 27:1065–1082, 2016.
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S.L.Cotter, G.O.Roberts, A.M.Stuart, D. White, MCMC methods for functions: modifying old algorithms to make them faster. Statistical Science, 28 (2013) 424-446).
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G.Cox, M.A.Christie, Fitting of a Multiphase Equation of State with Swarm Intelligence. Journal of Physics: Condensed Matter. (27) (2015) 405201.
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M.Dashti, K.J.H.Law, A.M.Stuart, J.Voss, MAP estimators and posterior consistency in Bayesian nonparametric inverse problems. Inverse Problems, 29(2013) 095017.
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A.B.Duncan, C.M.Elliot, G.A.Pavliotis, A.M.Stuart, A multiscale analysis of diffusions on rapidly varying surfaces. J. Nonlinear Science 25/2 (2015)389-449.
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M.M.Dunlop, M.A.Iglesias, A.M.Stuart, Hierarchical Bayesian level set inversion. Statistics and Computing (2016).
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M.M.Dunlop, A.M.Stuart, The Bayesian formulation of EIT: analysis and algorithms. Inverse Problems and Imaging 10(4)(2016) 1007-1036.
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M.M.Dunlop, A.M.Stuart, MAP estimators for piecewise continuous inversion. Inverse Problems 32(10) (2016) 105003.
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L.Ellam, N.Zabaras, M.Girolami. A Bayesian Approach to Multiscale Inverse Problems with On-the-fly Scale Determination. Journal of Computational Physics, 326, 115-140, 2016.
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D.Elsakout, M.Christie, G.Lord, Multilevel Markov Chain Monte Carlo (MLMCMC) For Uncertainty Quantification. SPE North Africa Technical Conference and Exhibition, 14-16 September, Cairo, Egypt. Society of Petroleum Engineers. (2015)
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M.Filiponne, M.Girolami, Pseudo-Marginal Bayesian Inference for Gaussian Processes. IEEE Transactions Pattern Analysis and Machine Intelligence, 36(11) (2014) 2214-2226.
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M.Girolami, A.-M.Lynne, H.Strathmann, D.Simpson, Y.Atchade.On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods Statistical Science, Volume 30, Number 4 (2015), 443-467, 2015.
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M.Hairer, A.M.Stuart, S.J.Vollmer, Spectral Gaps for a Metropolis–Hastings Algorithm in Infinite Dimensions. The Annals of Applied Probability, 24/6(2014), 2455-2490.
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C.Handley, M.A.Christie, Calibrating reaction rates for the CREST model, Paper presented at 19th Biennial Conference on Shock Compression of Condensed Matter, Tampa, United States, 14/06/15 - 19/06/15 (2015).
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P.Hennig, M.A.Osborne, M.Girolami, Probabilistic Numerics and Uncertainty in Computations. Proceedings of the Royal Society A, Proc. R. Soc. A 2015 471 20150142; DOI: 10.1098/rspa.2015.0142.
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V.H.Hoang, K.J.H.Law, A.M.Stuart, Determining white noise forcing from Eulerian observations in the Navier-Stokes equation. Stochastic PDEs: Analysis and Computation, 2(2014), 233-261.
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V.H.Hoang, C.Schwab, A.M.Stuart, Complexity analysis of accelerated MCMC methods for Bayesian inversion. Inverse Problems, 29/8 (2013) 085010
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J.J.J.Hutahaean, M.A.Christie, V.Demyanov, On Optimal Selection of Objective Grouping for Multi-Objective History Matching, SPE Journal (2017).

J.J.J.Hutahaean, V.Demyanov, M.A.Christie, Many-objective optimization algorithm applied to history matching. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), (2017) IEEE. DOI: 10.1109/SSCI.2016.7850215.
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M.A.Iglesias, K.J.H.Law, A.M.Stuart, Evaluation of Gaussian approximations for data assimilation in reservoir models. Computational Geosciences. 17(2013), 851-885.
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M.A.Iglesias, K.J.H.Law, A.M.Stuart, Ensemble Kalman Methods for Inverse Problems. Inverse Problems, 29(2013) 045001.
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M.A.Iglesias, K.Lin, A.M.Stuart, Well-posed Bayesian geometric inverse problems arising in subsurface flow. Inverse Problems, 30 (2014) 114001.
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M.Iglesias, Y.Lu, A.M.Stuart, A Bayesian level set method for geometric inverse problems. Interfaces and Free Boundaries 18 (2016), 181-217.
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M.Iglesias, A.M.Stuart. Inverse Problems and Uncerrainty Quantification. SIAM News, July/August 2014.
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D.T.B.Kelly, K.J.H.Law, A.M.Stuart, Well-posedness and accuracy of the ensemble Kalman filter In discrete and continuous time. Nonlinearity, 27 (2014) 2579-2603.
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S.Lan, T.Bui-Thanh, M.Christie, M.Girolami, Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for Bayesian Inverse Problems. Journal of Computational Physics, Volume 308, 1 March 2016, Pages 81-101.
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K.J.H.Law, D.Sanz-Alonso, A.Shukla, A.M.Stuart, Filter accuracy for the Lorenz 96 model: fixed versus adaptive observation operators. Physica D: Nonlinear Phenomena 325 (2016) 1-13.
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K.J.H.Law, A.Shukla, A.M.Stuart, Analysis of the 3DVAR Filter for the Partially Observed Lorenz '63 Model. Discrete and Continuous Dynamical Systems A, 34(2014), 1061-1078.
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W.Lee, A.M.Stuart, Derivation and analysis of simplified filters for complex dynamical systems. Communications in Mathematical Sciences 15(2) (2017), 413-450.
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C.Oates, M.Girolami (2016) Control Functionals for Quasi-Monte Carlo Integration. Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS).
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C.Oates, M.Girolami, N.Chopin, Control Functionals for Monte Carlo Integration. Journal of Royal Statistical Society - Series B, Volume 79, Issue 3, Pages 695–718, 2017.
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C.Oates, T.Papamarkou, M.Girolami. The Controlled Thermodynamic Integral, Journal of the American Statistical Association, Volume 111, Number 514, 634--645, 2016.
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M.Ottobre, N.S.Pillai, F.J.Pinski, A.M.Stuart, A function space HMC algorithm with second order Langevin diffusion limit. Bernoulli 22/1(2016) 60-106.
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N.S.Pillai, A.M.Stuart, A.H. Thiery, Noisy gradient flow from a random walk in Hilbert space. Stochastic PDEs: Analysis and Computation, 2(2014), 196-232.
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F.J.Pinski, G.Simpson, A.M.Stuart, H.Weber, Algorithms for Kullback-Leibler approximation for probability measures in infinite dimensions. SIAM J. Sci. Comp. 37/6 (2015) A2733–A2757.
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F.J.Pinski, G.Simpson, A.M.Stuart, H.Weber, Kullback-Leibler approximation for probability measures on infinite dimensional spaces. SIAM J. Mathematical Analysis 47(2015) 4091-4122.
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Y.Pokern, A.M.Stuart, J.H.Van Zanten, Posterior consistency via precision operators for nonparametric drift estimation in SDEs. Stochastic Processes and Their Applications, 123/2 (2013) 603-628.
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S.Reich, A.M.Stuart. Data Assimilation: New Challenges in Random and Stochastic Dynamical Systems. SIAM News, October and November 2015.
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D.Sanz-Alonso, A.M.Stuart, Long-Time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems. SIAM/ASA J. Uncertainty Quantification, 3(1) (2015) 1200-1220.
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R.Scheichl, A.M.Stuart, A.L.Teckentrup, Quasi Monte-Carlo and multi-level Monte Carlo for computing posterior expectations in elliptic inverse problems. SIAM/ASA J. Uncertainty Quantification, 5(1), 493-518, 2017.
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C.Schillings, M.Sunnåker, J.Stelling, Ch.Schwab, Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks, PLOS Comp. Biol., 2015.
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