Wang, H., Ao, Z., Yu, T. and Li, J., 2021. Inverse Gaussian Process regression for likelihood-free inference. arXiv preprint arXiv:2102.10583.
Yu, T., Wang, H. and Li, J., 2019. Maximizing conditional entropy of Hamiltonian Monte Carlo sampler. arXiv preprint arXiv:1910.05275.
Zhou, Q., Yu, T., Zhang, X. and Li, J., 2020. Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data. SIAM Journal on Imaging Sciences, 13(1), pp.29-52.
Wang, H. and Li, J., 2018. Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions. Neural computation, 30(11), pp.3072-3094.
Hu, Z., Yao, Z. and Li, J., 2017. On an adaptive preconditioned Crank–Nicolson MCMC algorithm for infinite dimensional Bayesian inference. Journal of Computational Physics, 332, pp.492-503.
Wu, K. and Li, J., 2016. A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification. Journal of Computational Physics, 321, pp.1098-1109.
Yao, Z., Hu, Z. and Li, J., 2016. A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations. Inverse Problems, 32(7), p.075006.
Li, J. and Marzouk, Y.M., 2014. Adaptive construction of surrogates for the Bayesian solution of inverse problems. SIAM Journal on Scientific Computing, 36(3), pp.A1163-A1186.
Li, J., Li, J. and Xiu, D., 2011. An efficient surrogate-based method for computing rare failure probability. Journal of Computational Physics, 230(24), pp.8683-8697.
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