Some properties of the divergence measure of information content as related to quantitative analyses

1982 ◽  
Vol 47 (4) ◽  
pp. 1195-1202
Author(s):  
Karel Eckschlager ◽  
Vladimír Štěpánek
Keyword(s):  
Quantitative Analysis ◽  
Information Content ◽  
Shannon’S Entropy ◽  
Divergence Measure ◽  
Shannon's Entropy ◽  
Quantitative Analyses ◽  
The Difference

Some properties of the divergence measure of the information content of quantitative analysis results are introduced and it is shown how this measure describes the effect of the difference of a result obtained by the analysis from a preliminary one or from an anticipated value. In the same time the measure is compared to the decrease of uncertainty as expressed in terms of Shannon's entropy.

How to measure information obtained from biased quantitative analyses

1985 ◽  
Vol 50 (6) ◽  
pp. 1359-1367 ◽  
Author(s):  
Karel Eckschlager ◽  
Vladimír Štěpánek
Keyword(s):  
Quantitative Analysis ◽  
Systematic Error ◽  
Information Content ◽  
Information Measure ◽  
Divergence Measure ◽  
Mean Error ◽  
Quantitative Analyses ◽  
Relationship Of ◽  
The Relationship

It is shown that the use of the divergence measure for expressing the information content of the results of a quantitative analysis is justified only then if we need not to expect the rise of a systematic, i.e., statistically significant mean error. However, unless we can exclude the rise of a systematic error we have to adopt the information measure I(r, p, p0) proposed earlier; then of course every even statistically insignificant mean error has effect upon the information content. It is studied the relationship of this measure to another information measure used in a specific case for expressing the information content of biased results of a quantitative analysis.

Information content of the determinations of the amount of an analyte

1988 ◽  
Vol 53 (12) ◽  
pp. 3021-3028 ◽  
Author(s):  
Karel Eckschlager ◽  
Jiří Fusek
Keyword(s):  
Reference Material ◽  
Information Content ◽  
Analytical Method ◽  
Analytical Signal ◽  
Divergence Measure ◽  
Analytical Results

It is shown how the information content expressed in terms of the extended divergence measure I(r; p, p0) depends on the amount of the component to be determined when homoscedastic and heteroscedastic dependences of the analytical signal on the amount of the analyte are distinguished. The importance of the accuracy is pointed out, with which the amount of the analyte is known in a reference material employed for testing a particular analytical method. Rules are set, the maintaining of which is aimed at avoiding the origin of null information content of analytical results.

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

scholarly journals ASSESSMENT OF URBAN SPRAWL USING SHANNON’S ENTROPY AND FRACTAL ANALYSIS: A CASE STUDY OF ATAKUM, ILKADIM AND CANIK (SAMSUN, TURKEY)

2017 ◽  
Vol 25 (3) ◽  
pp. 264-276 ◽  
Author(s):  
Derya OZTURK
Keyword(s):  
Urban Sprawl ◽  
Fractal Analysis ◽  
Urban Form ◽  
Entropy Method ◽  
Shannon’S Entropy ◽  
Analysis Method ◽  
Maximum Likelihood Classification ◽  
Shannon's Entropy ◽  
Box Counting

Urban sprawl is one of the most important problems in urban development due to its negative environmental and societal impacts. Therefore, the spatial pattern of urban growth should be accurately analyzed and well understood for effective urban planning. This paper focuses on urban sprawl analysis in the Atakum, Ilkadim and Canik districts of Samsun, Turkey. In this study, urban sprawl was examined over a period of 24 years using Shannon's entropy and fractal analysis based on remote sensing and Geographic Information System (GIS). The built-up areas in 1989, 2000 and 2013 were extracted from Landsat TM/ETM+/OLI images using the maximum likelihood classification method, and urban form changes in the 1989–2013 period were investigated. The Shannon's entropy method was used to determine the degree of urban sprawl, and a fractal analysis method based on box counting was used to characterize the urban sprawl. The results show that Atakum, Ilkadim and Canik experienced important changes and have considerable sprawl and complex characteristics now. The study also revealed that there is no monotonic relationship between Shannon's entropy and fractal dimension.

Markedness and Perspective in the Interpretation of the Active and Passive Voice

1974 ◽  
Vol 26 (2) ◽  
pp. 189-195 ◽  
Author(s):  
Irene Klenbort ◽  
Moshe Anisfeld
Keyword(s):  
Information Content ◽  
Focal Point ◽  
The Other ◽  
Clear Pattern ◽  
Passive Voice ◽  
Other Hand ◽  
Logical Subject ◽  
The Difference

The subjects were presented with active and passive sentences. For each sentence, they had to choose between two alternative implications. The pattern of choices indicates that in the passive the logical subject was interpreted by the subjects as the focal point of the information asserted by the sentence and as the carrier of overall responsibility for the sentential proposition. In contrast to the passive, there was no clear pattern of preferences for the active. The difference between the two voices was attributed to their markedness asymmetry, the passive being marked and the active unmarked. It is concluded that the active offers a neutral structure for conveying information; a structure available for use when one does not want to superimpose on the information content any stylistic or connotational implications. The passive, on the other hand, suggests special connotations in addition to the basic message.

Quantitative Models for Complex Physical Systems

2014 ◽  
Vol 1061-1062 ◽  
pp. 1144-1147
Author(s):  
Jun Fu ◽  
Jin Zhao Wu ◽  
Ning Zhou ◽  
Hong Yan Tan
Keyword(s):  
Quantitative Analysis ◽  
State Space ◽  
Control Systems ◽  
Chemical Reaction ◽  
Metric Space ◽  
Manufacturing Systems ◽  
Quantitative Model ◽  
Hybrid Automata ◽  
Physical Systems ◽  
The Difference

We present a quantitative model, called metric hybrid automata, for quantifying the behaviors of complex physical systems, such as chemical reaction control systems, manufacturing systems etc. Due to the introduction of a metric, the state space of hybrid automata forms a metric space, in which the difference of states can be quantified. Furthermore, in order to reveal the distance of system behaviors, we construct the simulation distance and the bisimulation distance, which quantify the similarity of system behaviors. Our model provides the basis for quantitative analysis for those complex physical systems.

scholarly journals Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3917
Author(s):  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim ◽  
Ji-Soo Hwang
Keyword(s):  
Quantitative Analysis ◽  
Fluorescence Detection ◽  
High Performance ◽  
Low Cost ◽  
Dna Amplification ◽  
Fluorescent Detection ◽  
Rt Pcr ◽  
Commercial System ◽  
Intensity Changes ◽  
The Difference

Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera’s performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance.

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