Results 81 to 90 of about 4,779,838 (300)
Variance Reduction Optimization Algorithm Based on Random Sampling [PDF]
Jisuanji kexue yu tansuoThe stochastic gradient descent (SGD) algorithms have been applied to machine learning and deep learning due to their superior performance. However, SGD requires the stochastic gradient of a single sample to approximate the full gradient of all samples ...
GUO Zhenhua, YAN Ruidong, QIU Zhiyong, ZHAO Yaqian, LI Rengang
doaj +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Introduction This final analysis of a multicenter, prospective postmarketing surveillance study evaluated the safety of daprodustat in patients with chronic kidney disease anemia in routine clinical practice in Japan. Methods Patients who initiated daprodustat between September 2020 and July 2022 were registered.
Tadao Akizawa +7 more
wiley +1 more source
Variance-of-variance risk premium
, 2018 This article explores the premium for bearing the variance risk of the VIX index, called the variance-of-variance risk premium. I find that during the sample period from 2006 until 2014 trading strategies exploiting the difference between the implied and
Andreas Kaeck (4464625)
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
wiley +1 more source
Linear statistical inference for global and local minimum variance portfolios [PDF]
Traditional portfolio optimization has been often criticized since it does not account for estimation risk. Theoretical considerations indicate that estimation risk is mainly driven by the parameter uncertainty regarding the expected asset returns rather
Frahm, Gabriel
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L2 Model Reduction and Variance Reduction
, 2007 In this contribution we examine certain variance properties of model reduction. The focus is on L2 model reduction, but some general results are also presented. These general results can be used to analyze various other model reduction schemes.
Ljung, Lennart, Tjärnström, Fredrik
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A variance-reduction strategy for the sensitivity of βeff [PDF]
EPJ Web of ConferencesThe Monte Carlo computation of the GPT-based sensitivity of the effective delayed neutron fraction βeff to nuclear data proves to be quite difficult to converge due to the small amount of delayed neutrons that are sampled in k-eigenvalue calculations ...
Jinaphanh Alexis, Zoia Andrea
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Reusing Samples in Variance Reduction
CoRRWe provide a general framework to improve trade-offs between the number of full batch and sample queries used to solve structured optimization problems. Our results apply to a broad class of randomized optimization algorithms that iteratively solve sub-problems to high accuracy.
Yujia Jin +3 more
openaire +3 more sources
FEBS Letters, EarlyView.Enteropathogenic E. coli (EPEC) infects the human intestinal epithelium, resulting in severe illness and diarrhoea. In this study, we compared the infection of cancer‐derived cell lines with human organoid‐derived models of the small intestine. We observed a delayed in attachment, inflammation and cell death on primary cells, indicating that host ...
Mastura Neyazi +5 more
wiley +1 more source
Variance reduction in MCMC [PDF]
We propose a general purpose variance reduction technique for MCMC estimators. The idea is obtained by combining standard variance reduction principles known for regular Monte Carlo simulations (Ripley, 1987) and the Zero-Variance principle introduced in
Bressanini Dario +2 more
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