Optimal sampling distribution and weighting for ensemble averages

1999 ◽  
Author(s):  
B.H. Brumley ◽  
K.L. Deines
Keyword(s):  
Sampling Distribution ◽  
Optimal Sampling

The comparison of the diversity of activated sludge plants

1998 ◽  
Vol 37 (4-5) ◽  
pp. 71-78 ◽  
Author(s):  
Thomas P. Curtis ◽  
Noel G. Craine
Keyword(s):  
Cluster Analysis ◽  
Activated Sludge ◽  
Treatment Process ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Optimal Sampling ◽  
Bacterial Populations ◽  
Large Numbers ◽  
Probable Presence ◽  
Gradient Gel Electrophoresis ◽  
Sampling Regime

The explicit engineering of bacterial populations requires that we know which organisms perform which tasks. The comparison of the bacterial diversity of activated sludge plants may give important information about the functions of different bacteria. This difficult task may be made easier by the use of technologies based on 16S rRNA based techniques. In this study we have used denaturing gradient gel electrophoresis (DGGE) to determine the optimal sampling regime for comparative studies and used cluster analysis to show how plants may be quantitatively compared. We sought evidence of spatial, diurnal and intrasample variation in a number of sites. No evidence for variation was found in the plants studied and we concluded that a single sample of an activated sludge plant was sufficient for a plant to plant comparison. The cluster analysis was able to distinguish between plants, though further work is required to find the most appropriate basis for such comparisons. We found organisms from raw sewage in the mixed liquor samples, these organisms may have no functional significance in the treatment process and thus complicate plant to plant comparisons as will the probable presence of heteroduplex rDNA products. Nevertheless we believe that these drawbacks do not outweigh the advantages of being able to take and compare relatively large numbers of samples.

scholarly journals Optimal sampling of dynamical large deviations via matrix product states

2021 ◽  
Vol 103 (6) ◽  
Author(s):  
Luke Causer ◽  
Mari Carmen Bañuls ◽  
Juan P. Garrahan
Keyword(s):  
Large Deviations ◽  
Matrix Product ◽  
Optimal Sampling ◽  
Matrix Product States

scholarly journals Asymptotic Inference for Optimal Rerandomization Designs

2021 ◽  
Vol 1 (1) ◽  
pp. 49-58
Author(s):  
Mårten Schultzberg ◽  
Per Johansson
Keyword(s):  
Experimental Design ◽  
Normal Distribution ◽  
Mahalanobis Distance ◽  
Design Strategy ◽  
Sampling Distribution ◽  
Optimal Designs ◽  
Asymptotic Inference ◽  
Optimal Allocations ◽  
Asymptotic Sampling

AbstractRecently a computational-based experimental design strategy called rerandomization has been proposed as an alternative or complement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to a balance criterion, and then randomize within the set of acceptable allocations. Based on the Mahalanobis distance criterion for balancing the covariates, we show that asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations. Finally, we show that for the optimal and near optimal designs, the quite complex asymptotic sampling distribution derived by Li et al. (2018), is well approximated by a normal distribution.

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