Bayesian Optimization for Adaptive Experimental Design: A Review
نویسندگان
چکیده
منابع مشابه
Bayesian Experimental Design: A Review
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive o...
متن کاملAdaptive Hamiltonian Estimation Using Bayesian Experimental Design
Using Bayesian experimental design techniques, we have shown that for a single twolevel quantum mechanical system under strong (projective) measurement, the dynamical parameters of a model Hamiltonian can be estimated with exponentially improved accuracy over offline estimation strategies. To achieve this, we derive an adaptive protocol which finds the optimal experiments based on previous obse...
متن کاملBayesian Optimization for Adaptive MCMC
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabili...
متن کاملBayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient ...
متن کاملExperimental adaptive Bayesian tomography
K. S. Kravtsov,1 S. S. Straupe,2,* I. V. Radchenko,1 N. M. T. Houlsby,3 F. Huszár,3 and S. P. Kulik2 1A. M. Prokhorov General Physics Institute RAS, Moscow, Russia 2Faculty of Physics, M. V. Lomonosov Moscow State University, Moscow, Russia 3Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom (Received 26 March 2013; pu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2966228