scholarly journals Study on the Confidence and Reliability of the Mean Seismic Probability Risk Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Xiao-Lei Wang ◽  
Da-Gang Lu
Keyword(s):  
Seismic Design ◽  
Confidence Level ◽  
Risk Model ◽  
Safety Evaluation ◽  
Design Parameters ◽  
Critical Infrastructures ◽  
High Confidence ◽  
Confidence Levels ◽  
The Mean ◽  
Mean Risk

The mean seismic probability risk model has widely been used in seismic design and safety evaluation of critical infrastructures. In this paper, the confidence levels analysis and error equations derivation of the mean seismic probability risk model are conducted. It has been found that the confidence levels and error values of the mean seismic probability risk model are changed for different sites and that the confidence levels are low and the error values are large for most sites. Meanwhile, the confidence levels of ASCE/SEI 43-05 design parameters are analyzed and the error equation of achieved performance probabilities based on ASCE/SEI 43-05 is also obtained. It is found that the confidence levels for design results obtained using ASCE/SEI 43-05 criteria are not high, which are less than 95%, while the high confidence level of the uniform risk could not be achieved using ASCE/SEI 43-05 criteria and the error values between risk model with target confidence level and mean risk model using ASCE/SEI 43-05 criteria are large for some sites. It is suggested that the seismic risk model considering high confidence levels instead of the mean seismic probability risk model should be used in the future.

scholarly journals An analytical method of estimating Value-at-Risk on the Belgrade Stock Exchange

2009 ◽  
Vol 54 (183) ◽  
pp. 119-138 ◽  
Author(s):  
Milica Obadovic ◽  
Mirjana Obadovic
Keyword(s):  
At Risk ◽  
Failure Rate ◽  
Confidence Level ◽  
Analytical Method ◽  
Value At Risk ◽  
Risk Evaluation ◽  
Risk Model ◽  
Stock Exchange ◽  
Confidence Levels ◽  
The Given

This paper presents market risk evaluation for a portfolio consisting of shares that are continuously traded on the Belgrade Stock Exchange, by applying the Value-at-Risk model - the analytical method. It describes the manner of analytical method application and compares the results obtained by implementing this method at different confidence levels. Method verification was carried out on the basis of the failure rate that demonstrated the confidence level for which this method was acceptable in view of the given conditions.

scholarly journals Cosmic Ray Anisotropy above 1015 eV

1987 ◽  
Vol 40 (3) ◽  
pp. 423 ◽  
Author(s):  
RW Clay
Keyword(s):  
Internal Consistency ◽  
Confidence Level ◽  
Cosmic Ray ◽  
Published Data ◽  
High Confidence ◽  
Real Effect ◽  
Confidence Levels ◽  
High Confidence Level

An examination is made of published data on cosmic ray anisotropy at energies above about 1015 eV. Both amplitude and phase results are examined in an attempt to assess the confidence which can be placed in the observations as a whole. It is found that whilst many published results individually may suggest quite high confidence levels of real measured anisotropy, the data taken as a whole are less convincing. Some internal consistency in the phase results suggests that a real effect may have been measured but, again, this is not at a high confidence level.

scholarly journals A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 115-120
Author(s):  
Dejin Tang ◽  
Xiaoming Zhou ◽  
Jie Jiang ◽  
Caiping Li
Keyword(s):  
Confidence Level ◽  
Spatial Location ◽  
Point Clouds ◽  
Airborne Lidar ◽  
High Confidence ◽  
Confidence Levels ◽  
Segment Distribution ◽  
Adjacent Segments ◽  
Airborne Lidar Data ◽  
High Confidence Level

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

scholarly journals GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem

2020 ◽  
Vol 36 (12) ◽  
pp. 3833-3840
Author(s):  
Ming-Ju Tsai ◽  
Jyun-Rong Wang ◽  
Shinn-Jang Ho ◽  
Li-Sun Shu ◽  
Wen-Lin Huang ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Confidence Level ◽  
Regulatory Network ◽  
Supplementary Information ◽  
Experimental Measurements ◽  
Confidence Levels ◽  
Benchmark Datasets ◽  
The Mean ◽  
Gene Regulatory ◽  
Evolutionary Intelligence

Abstract Motivation Non-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements. Results This work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets. Availability and implementation All of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

2021 ◽  
Author(s):  
Masaru Shirasuna ◽  
Hidehito Honda
Keyword(s):  
Binary Choice ◽  
Wisdom Of Crowds ◽  
High Confidence ◽  
Behavioral Experiments ◽  
Judgment Accuracy ◽  
Confidence Levels ◽  
Group Judgment ◽  
The Mean ◽  
Practical Implications ◽  
The Wisdom Of Crowds

Abstract In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

scholarly journals A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 115-120
Author(s):  
Dejin Tang ◽  
Xiaoming Zhou ◽  
Jie Jiang ◽  
Caiping Li
Keyword(s):  
Confidence Level ◽  
Spatial Location ◽  
Point Clouds ◽  
Airborne Lidar ◽  
High Confidence ◽  
Confidence Levels ◽  
Segment Distribution ◽  
Adjacent Segments ◽  
Airborne Lidar Data ◽  
High Confidence Level

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

Dynamic Response and Buckling Failure Measures for Structures With Bounded and Random Imperfections

1991 ◽  
Vol 58 (4) ◽  
pp. 1092-1095 ◽  
Author(s):  
H. E. Lindberg
Keyword(s):  
Standard Deviation ◽  
Dynamic Response ◽  
Confidence Level ◽  
Maximum Response ◽  
Statistical Distributions ◽  
High Confidence ◽  
Confidence Levels ◽  
Buckling Failure ◽  
Failure Avoidance ◽  
To Come

Comparisons between an unknown-but-bounded imperfection model and a random imperfection model show that for simple pointwise failure measures, at least, the two models give the same expressions for their measures of response, but each measure has a distinctly different interpretation. The former gives the maximum possible response for any imperfection within a specified bound. The latter gives the standard deviation of response, which, together with the statistical distribution, can be used to specify the maximum response at a specified confidence level. However, since the statistical distributions of imperfections, and hence of the response are often unknown, confidence levels are difficult to define, especially in the tail of the distribution at high confidence levels. The unknown-but-bounded model requires less information about the imperfections to come to a well-defined bound on response. It is further shown that, while the maximum possible response might seem to be a severe failure avoidance criterion, it can be less constricting than having to impose artificially high confidence levels with poorly known statistical distributions.

scholarly journals Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

2021 ◽  
Author(s):  
Masaru Shirasuna ◽  
Hidehito Honda
Keyword(s):  
Binary Choice ◽  
Wisdom Of Crowds ◽  
High Confidence ◽  
Behavioral Experiments ◽  
Judgment Accuracy ◽  
Confidence Levels ◽  
Group Judgment ◽  
The Mean ◽  
Practical Implications ◽  
The Wisdom Of Crowds

In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

scholarly journals Side- and similarity-biases during confidence conformity

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253577
Author(s):  
Winny W. Y. Yue ◽  
Kiyofumi Miyoshi ◽  
Wendy W. S. Yue
Keyword(s):  
Confidence Rating ◽  
Confidence Level ◽  
Human Motion ◽  
Social Situations ◽  
High Confidence ◽  
Confidence Levels ◽  
Memory Accuracy ◽  
Memory Conformity ◽  
Subjective Confidence ◽  
Gradual Accumulation

Memory conformity may develop when people are confronted with distinct memories reported by others in social situations and knowingly/unknowingly adhere to these exogenous memories. Earlier research on memory conformity suggests that (1) subjects were more likely to conform to confederate with high confidence; (2) subjects with low confidence on their memory accuracy were more likely to conform, and; (3) this subjective confidence could be adjusted by social manipulations. Nonetheless, it remains unclear how the confidence levels of ours and others may interact and produce a combined effect on our degree of conformity. More importantly, is memory conformity, defined by a complete adoption of the opposite side, the result of a gradual accumulation of subtler changes at the confidence level, i.e., a buildup of confidence conformity? Here, we followed participant’s confidence transformation quantitatively over three confederate sessions in a memory test. After studying a set of human motion videos, participants had to answer simultaneously whether a target or lure video had appeared before by indicating their side (i.e., Yes/No) and their associated confidence rating. Participants were allowed to adjust their responses as they were being shown randomly-generated confederates’ answers and confidence values. Results show that participants indeed demonstrated confidence conformity. Interestingly, they tended to become committed to their side early on and gain confidence gradually over subsequent sessions. This polarizing behaviour may be explained by two kinds of preferences: (1) Participant’s confidence enhancement towards same-sided confederates was greater in magnitude compared to the decrement towards an opposite-sided confederate; and (2) Participants had the most effective confidence boost when the same-sided confederates shared similar, but not considerably different, confidence level to theirs. In other words, humans exhibit side- and similarity-biases during confidence conformity.

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