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Volumn 2015-January, Issue , 2015, Pages 3605-3611

Bayesian active learning for posterior estimation

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; BAYESIAN; GAUSSIAN PROCESSES; GENERATING SAMPLES; POSTERIOR ESTIMATIONS; QUERY STRATEGIES; REAL EXAMPLE;

EID: 84949748958     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (59)

References (29)
  • 1
    • 85098099582 scopus 로고    scopus 로고
    • The Gaussian process density sampler
    • Ryan Prescott Adams, Iain Murray, and David J. C. MacKay. The Gaussian Process Density Sampler. In NIPS, 2008.
    • (2008) NIPS
    • Adams, R.P.1    Murray, I.2    MacKay, D.J.C.3
  • 2
    • 84869826137 scopus 로고    scopus 로고
    • A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
    • Eric Brochu, Vlad M. Cora, and Nando de Freitas. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. CoRR, 2010.
    • (2010) CoRR
    • Brochu, E.1    Cora, V.M.2    De Freitas, N.3
  • 4
    • 35348925480 scopus 로고    scopus 로고
    • Scrutinizing exotic cosmological models using ESSENCE supernova data combined with other cosmological probes
    • T. M. Davis et al. Scrutinizing Exotic Cosmological Models Using ESSENCE Supernova Data Combined with Other Cosmological Probes. The Astrophysical Journal, pages 716-725, 2007.
    • (2007) The Astrophysical Journal , pp. 716-725
    • Davis, T.M.1
  • 7
    • 84856462978 scopus 로고    scopus 로고
    • Adaptive submodularity: Theory and applications in active learning and stochastic optimization
    • Daniel Golovin and Andreas Krause. Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. Journal of Artificial Intelligence Research (JAIR), 2011.
    • (2011) Journal of Artificial Intelligence Research (JAIR)
    • Golovin, D.1    Krause, A.2
  • 12
    • 41549146576 scopus 로고    scopus 로고
    • Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies
    • Andreas Krause, Ajit Singh, and Carlos Guestrin. Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies. J. Mach. Learn. Res., 2008.
    • (2008) J. Mach. Learn. Res.
    • Krause, A.1    Singh, A.2    Guestrin, C.3
  • 19
    • 0026189315 scopus 로고
    • Bayesian approach to global optimization and application to multiobjective and constrained problems
    • J.B. Mockus and L.J. Mockus. Bayesian approach to global optimization and application to multiobjective and constrained problems. Journal of Optimization Theory and Applications, 1991.
    • (1991) Journal of Optimization Theory and Applications
    • Mockus, J.B.1    Mockus, L.J.2
  • 22
    • 84867997236 scopus 로고    scopus 로고
    • On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
    • Gareth W. Peters, Y. Fan, and Scott A. Sisson. On sequential Monte Carlo, partial rejection control and approximate Bayesian computation. Statistics and Computing, 2012.
    • (2012) Statistics and Computing
    • Peters, G.W.1    Fan, Y.2    Sisson, S.A.3
  • 27
    • 35148901361 scopus 로고    scopus 로고
    • Nested sampling for general Bayesian computation
    • John Skilling. Nested sampling for general Bayesian computation. Bayesian Anal., 2006.
    • (2006) Bayesian Anal.
    • Skilling, J.1
  • 29
    • 33845429601 scopus 로고    scopus 로고
    • Cosmological constraints from the SDSS luminous red galaxies
    • December
    • M. Tegmark et al. Cosmological Constraints from the SDSS Luminous Red Galaxies. Physical Review, December 2006.
    • (2006) Physical Review
    • Tegmark, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.