scholarly journals Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo

2018 ◽  
Vol 13 (1) ◽  
pp. 139-162 ◽  
Author(s):  
Christopher C. Drovandi ◽  
Minh-Ngoc Tran
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Design Of Experiments ◽  
Optimal Design Of Experiments ◽  
Quasi Monte Carlo ◽  
Bayesian Optimal Design ◽  
Fully Bayesian

scholarly journals Discussion: “Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions” (Pandita, P., Bilionis, I., and Panchal, J., 2019. ASME. J. Mech. Des. 141(10): 101404)

2021 ◽  
pp. 1-5
Author(s):  
Xianliang Gong ◽  
Yulin Pan
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Black Box ◽  
Optimal Design Of Experiments ◽  
Analytical Computation ◽  
Input Distribution ◽  
Bayesian Optimal Design ◽  
Statistical Expectation ◽  
Intuitive Interpretation

Abstract The authors of the discussed paper simplified the information-based acquisition on estimating statistical expectation and developed analytical computation for each involved quantity under uniform input distribution. In this discussion, we show that (1) the last three terms of the acquisition always add up to zero, leaving a concise form with a much more intuitive interpretation of the acquisition; (2) the analytical computation of the acquisition can be generalized to arbitrary input distribution, greatly broadening the application of the developed framework.

scholarly journals Maximal Power Tests for Detecting Defects in Meiotic Recombination

Genetics ◽  
2002 ◽  
Vol 161 (3) ◽  
pp. 1333-1337
Author(s):  
Thomas I Milac ◽  
Frederick R Adler ◽  
Gerald R Smith
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Meiotic Recombination ◽  
Region Of Interest ◽  
Genetic Distances ◽  
Genomic Region ◽  
Maximal Power ◽  
Optimal Design Of Experiments ◽  
Crossover Interference ◽  
Single Interval

Abstract We have determined the marker separations (genetic distances) that maximize the probability, or power, of detecting meiotic recombination deficiency when only a limited number of meiotic progeny can be assayed. We find that the optimal marker separation is as large as 30–100 cM in many cases. Provided the appropriate marker separation is used, small reductions in recombination potential (as little as 50%) can be detected by assaying a single interval in as few as 100 progeny. If recombination is uniformly altered across the genomic region of interest, the same sensitivity can be obtained by assaying multiple independent intervals in correspondingly fewer progeny. A reduction or abolition of crossover interference, with or without a reduction of recombination proficiency, can be detected with similar sensitivity. We present a set of graphs that display the optimal marker separation and the number of meiotic progeny that must be assayed to detect a given recombination deficiency in the presence of various levels of crossover interference. These results will aid the optimal design of experiments to detect meiotic recombination deficiency in any organism.

A New Practical Route to ZSM-5 Nanoparticles and Optimization of Synthetic Parameters through D-optimal Design of Experiments

2015 ◽  
Vol 62 (9) ◽  
pp. 817-825 ◽  
Author(s):  
Saeed Soltanali ◽  
Rouein Halladj ◽  
Alimorad Rashidi ◽  
Mansour Bazmi ◽  
Saeed Khodabakhshi
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Optimal Design Of Experiments

Optimal design of experiments for estimating the time of death in forensic medicine

2018 ◽  
Vol 34 (12) ◽  
pp. 125005 ◽  
Author(s):  
Martin Weiser ◽  
Yvonne Freytag ◽  
Bodo Erdmann ◽  
Michael Hubig ◽  
Gita Mall
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Forensic Medicine ◽  
Time Of Death ◽  
Optimal Design Of Experiments

scholarly journals Optimal Design of Experiments in the Presence of Interference

10.1596/29656 ◽  
2018 ◽  
Author(s):  
Sarah Baird ◽  
J. Aislinn Bohren ◽  
Craig McIntosh ◽  
Berk Ozler
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Optimal Design Of Experiments
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