Automated Model Selection with Bayesian Quadrature
暂无分享,去创建一个
Roman Garnett | Michael A. Osborne | Jean-Francois Ton | Henry Chai | R. Garnett | Jean-Francois Ton | Henry Chai
[1] B. Carlin,et al. Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .
[2] Jonathan J. Forster,et al. Default Bayesian model determination methods for generalised linear mixed models , 2010, Comput. Stat. Data Anal..
[3] Aki Vehtari,et al. Bayesian model assessment and selection using expected utilities , 2001 .
[4] Roman Garnett,et al. Detecting damped Ly α absorbers with Gaussian processes , 2016, 1605.04460.
[5] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[6] Klaus Ritter,et al. Bayesian numerical analysis , 2000 .
[7] Carl E. Rasmussen,et al. Active Learning of Model Evidence Using Bayesian Quadrature , 2012, NIPS.
[8] A. O'Hagan,et al. Bayes–Hermite quadrature , 1991 .
[9] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[10] Aki Vehtari,et al. A survey of Bayesian predictive methods for model assessment, selection and comparison , 2012 .
[11] Roman Garnett,et al. Improving Quadrature for Constrained Integrands , 2018, AISTATS.
[12] A. Shapiro. Monte Carlo Sampling Methods , 2003 .
[13] Carl E. Rasmussen,et al. Bayesian Monte Carlo , 2002, NIPS.
[14] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[15] Kenji Fukumizu,et al. Convergence guarantees for kernel-based quadrature rules in misspecified settings , 2016, NIPS.
[16] Kenji Fukumizu,et al. Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings , 2017, Foundations of Computational Mathematics.
[17] Anton Schwaighofer,et al. Learning Gaussian processes from multiple tasks , 2005, ICML.
[18] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[19] A. Mira,et al. Efficient Bayes factor estimation from the reversible jump output , 2006 .
[20] Michael A. Osborne,et al. Probabilistic Integration: A Role in Statistical Computation? , 2015, Statistical Science.
[21] Roman Garnett,et al. Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature , 2014, NIPS.
[22] F. M. Larkin. Gaussian measure in Hilbert space and applications in numerical analysis , 1972 .
[23] Michael A. Osborne,et al. Probabilistic Integration: A Role for Statisticians in Numerical Analysis? , 2015 .
[24] Charles H. Bennett,et al. Efficient estimation of free energy differences from Monte Carlo data , 1976 .
[25] David S. Leslie,et al. A tutorial on bridge sampling , 2017, Journal of mathematical psychology.
[26] Carl E. Rasmussen,et al. Warped Gaussian Processes , 2003, NIPS.
[27] Xiao-Li Meng,et al. SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION , 1996 .
[28] R. Caflisch. Monte Carlo and quasi-Monte Carlo methods , 1998, Acta Numerica.
[29] Sumio Watanabe,et al. Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory , 2010, J. Mach. Learn. Res..
[30] Francis R. Bach,et al. On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions , 2015, J. Mach. Learn. Res..
[31] W. M. Wood-Vasey,et al. SDSS-III: MASSIVE SPECTROSCOPIC SURVEYS OF THE DISTANT UNIVERSE, THE MILKY WAY, AND EXTRA-SOLAR PLANETARY SYSTEMS , 2011, 1101.1529.
[32] S. Godsill. On the Relationship Between Markov chain Monte Carlo Methods for Model Uncertainty , 2001 .
[33] Robert Haining,et al. Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .
[34] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[35] L. M. M.-T.. Theory of Probability , 1929, Nature.
[36] Simo Särkkä,et al. A Bayes-Sard Cubature Method , 2018, NeurIPS.