Characterization of the Altered Zone Around a Fracture in Palmottu Natural Analogue

1995 ◽  
Vol 412 ◽  
Author(s):  
K. Hartikainen ◽  
H. Pietarila ◽  
K. Rasilainen ◽  
H. Nordman ◽  
T. Ruskeeniemi ◽  
...  
Keyword(s):  
Sample Size ◽  
Core Sample ◽  
Natural Fracture ◽  
Drill Core ◽  
Natural Analogue ◽  
Model Calculations ◽  
Helium Gas ◽  
Altered Zone ◽  
Drill Core Sample

AbstractA drill core sample penetrating at a natural fracture has been analysed by helium gas techniques and by α-autoradiography. Porosity and diffusivity profiles away from the fracture have been determined and compared with structural profiles. Model calculations are used to determine the effects of sample size on the measured porosities and diffusivities.

Characterization of the logistic and loglogistic distributions by extreme value related stability with random sample size

1987 ◽  
Vol 24 (04) ◽  
pp. 838-851 ◽  
Author(s):  
W. J. Voorn
Keyword(s):  
Positive Integer ◽  
Sample Size ◽  
Random Variable ◽  
Logistic Distribution ◽  
Size Distributions ◽  
Random Sample Size ◽  
Maximum Value ◽  
Maximum Stability ◽  
Independent Observations

Maximum stability of a distribution with respect to a positive integer random variable N is defined by the property that the type of distribution is not changed when considering the maximum value of N independent observations. The logistic distribution is proved to be the only symmetric distribution which is maximum stable with respect to each member of a sequence of positive integer random variables assuming value 1 with probability tending to 1. If a distribution is maximum stable with respect to such a sequence and minimum stable with respect to another, then it must be logistic, loglogistic or ‘backward' loglogistic. The only possible sample size distributions in these cases are geometric.

scholarly journals Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

2018 ◽  
Author(s):  
Arghavan Bahadorinejad ◽  
Ivan Ivanov ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
Robert S Chapkin ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sample Size ◽  
Microbial Communities ◽  
State Of The Art ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

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