Mixture of Polynomials Probability Distributions for Grouped Sample Data

2014 ◽  
pp. 129-144
Author(s):  
Barry R. Cobb
Keyword(s):  
Probability Distributions ◽  
Sample Data

Variation analysis driven by small-sample data for compliant aero-structure assembly

2019 ◽  
Vol 39 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Biao Mei ◽  
Weidong Zhu ◽  
Yinglin Ke ◽  
Pengyu Zheng
Keyword(s):  
Probability Distributions ◽  
Small Sample ◽  
Accurate Estimation ◽  
Production Volume ◽  
Analysis Method ◽  
Variation Analysis ◽  
Content Type ◽  
Aircraft Manufacturing ◽  
Sample Data ◽  
Structure Assembly

Purpose Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially prototype manufacturing, the probability distributions are hard to obtain, and only the small-sample data of variation sources can be consulted. Thus, this paper aims to propose a variation analysis method driven by small-sample data for compliant aero-structure assembly. Design/methodology/approach First, a hybrid assembly variation model, integrating rigid effects with flexibility, is constructed based on the homogeneous transformation and elasticity mechanics. Then, the bootstrap approach is introduced to estimate a variation source based on small-sample data. The influences of bootstrap parameters on the estimation accuracy are analyzed to select suitable parameters for acceptable estimation performance. Finally, the process of assembly variation analysis driven by small-sample data is demonstrated. Findings A variation analysis method driven by small-sample data, considering both rigid effects and flexibility, is proposed for aero-structure assembly. The method provides a good complement to traditional variation analysis methods based on probability distributions of variation sources. Practical implications With the proposed method, even if probability distribution information of variation sources cannot be obtained, accurate estimation of the assembly variation could be achieved. The method is well suited for aircraft assembly, especially in the stage of prototype manufacturing. Originality/value A variation analysis method driven by small-sample data is proposed for aero-structure assembly, which can be extended to deal with other similar applications.

Adaptive input data transformation for improved network reconstruction with information theoretic algorithms

2016 ◽  
Vol 15 (6) ◽  
Author(s):  
Venkateshan Kannan ◽  
Jesper Tegner
Keyword(s):  
Probability Distributions ◽  
Reconstruction Algorithm ◽  
Data Transformation ◽  
Numerical Estimation ◽  
Finite Sample ◽  
Information Theoretic ◽  
Adaptive Partitioning ◽  
Sample Data ◽  
Shared Information ◽  
Information Metric

AbstractWe propose a novel systematic procedure of non-linear data transformation for an adaptive algorithm in the context of network reverse-engineering using information theoretic methods. Our methodology is rooted in elucidating and correcting for the specific biases in the estimation techniques for mutual information (MI) given a finite sample of data. These are, in turn, tied to lack of well-defined bounds for numerical estimation of MI for continuous probability distributions from finite data. The nature and properties of the inevitable bias is described, complemented by several examples illustrating their form and variation. We propose an adaptive partitioning scheme for MI estimation that effectively transforms the sample data using parameters determined from its local and global distribution guaranteeing a more robust and reliable reconstruction algorithm. Together with a normalized measure (Shared Information Metric) we report considerably enhanced performance both for

Regression equations of probability plot correlation coefficient test statistics using machine learning

2020 ◽  
Author(s):  
Hyunjun Ahn ◽  
Sunghun Kim ◽  
Joohyung Lee ◽  
Jun-Haeng Heo
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Distribution Model ◽  
Regression Equations ◽  
Probability Plot ◽  
Sample Data

<p>In the extremes hydrology field, it is essential to find the probability distribution model that is most appropriate for the sample data to estimate the reasonable probability quantile. Depending on the assumed probability distribution model, the probability quantile could be estimated with quite different values. The probability plot correlation coefficient (PPCC) test is one of the goodness-of-fit tests for finding suitable probability distributions for a given sample. The PPCC test determines whether assumed probability distributions are acceptable for the sample data using correlation coefficients between sample data and theoretical quantiles of assumed probability distributions. The critical values for identification are presented as a two-dimensional table, depending on the sample size and the shape parameters of models, for a three-parameter probability distribution. In this study, the applicability and utility of machine learning in the hydrology field were examined. For the usability of the PPCC test, a regression equation was derived using a machine learning algorithm with two variables: sample size and shape parameter.</p>

Asteroid and Comet Impact Speeds upon Mars: Significance for Panspermia and the Supply of Organics

1997 ◽  
Vol 161 ◽  
pp. 197-201 ◽  
Author(s):  
Duncan Steel
Keyword(s):  
Probability Distributions ◽  
Regions Of Interest ◽  
Significant Fraction ◽  
Organic Chemicals ◽  
Impact Speed ◽  
The Mean ◽  
The Impact

AbstractWhilst lithopanspermia depends upon massive impacts occurring at a speed above some limit, the intact delivery of organic chemicals or other volatiles to a planet requires the impact speed to be below some other limit such that a significant fraction of that material escapes destruction. Thus the two opposite ends of the impact speed distributions are the regions of interest in the bioastronomical context, whereas much modelling work on impacts delivers, or makes use of, only the mean speed. Here the probability distributions of impact speeds upon Mars are calculated for (i) the orbital distribution of known asteroids; and (ii) the expected distribution of near-parabolic cometary orbits. It is found that cometary impacts are far more likely to eject rocks from Mars (over 99 percent of the cometary impacts are at speeds above 20 km/sec, but at most 5 percent of the asteroidal impacts); paradoxically, the objects impacting at speeds low enough to make organic/volatile survival possible (the asteroids) are those which are depleted in such species.

Reversion Toward the Mean Independently of Regression Toward the Mean

Methodology ◽  
2009 ◽  
Vol 5 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Merton S. Krause
Keyword(s):  
Random Error ◽  
Evaluation Studies ◽  
Internal Validity ◽  
Intervention Evaluation ◽  
Population Mean ◽  
Experimental Intervention ◽  
Sample Data ◽  
The Mean ◽  
Regression Toward The Mean

There is another important artifactual contributor to the apparent improvement of persons subjected to an experimental intervention which may be mistaken for regression toward the mean. This is the phenomenon of random error and extreme selection, which does not at all involve the population regression of posttest on pretest scores but involves a quite different and independent reversion of subjects’ scores toward the population mean. These two independent threats to the internal validity of intervention evaluation studies, however, can be detected and differentiated on the sample data of such studies.

Delivering Sustainable Water Systems Using Kalman Filter and Statistical Approach

2017 ◽  
Vol 4 (1) ◽  
pp. 41-52
Author(s):  
Dedy Loebis
Keyword(s):  
Kalman Filtering ◽  
Statistical Approach ◽  
Water Systems ◽  
Data Sets ◽  
Pressure Data ◽  
Burst Event ◽  
Sample Data ◽  
Other Information ◽  
Supply Systems ◽  
Frequency Features

This paper presents the results of work undertaken to develop and test contrasting data analysis approaches for the detection of bursts/leaks and other anomalies within wate r supply systems at district meter area (DMA)level. This was conducted for Yorkshire Water (YW) sample data sets from the Harrogate and Dales (H&D), Yorkshire, United Kingdom water supply network as part of Project NEPTUNE EP/E003192/1 ). A data analysissystem based on Kalman filtering and statistical approach has been developed. The system has been applied to the analysis of flow and pressure data. The system was proved for one dataset case and have shown the ability to detect anomalies in flow and pres sure patterns, by correlating with other information. It will be shown that the Kalman/statistical approach is a promising approach at detecting subtle changes and higher frequency features, it has the potential to identify precursor features and smaller l eaks and hence could be useful for monitoring the development of leaks, prior to a large volume burst event.

An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

2020 ◽  
Vol 3 (1) ◽  
pp. 10501-1-10501-9
Author(s):  
Christopher W. Tyler
Keyword(s):  
Belief Propagation ◽  
Probability Distributions ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Fusion Model ◽  
Cue Combination ◽  
Depth Estimate ◽  
Appropriate Behavior ◽  
Depth Cues ◽  
Bayesian Averaging

Abstract For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the “belief propagation” algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or “modified weak fusion” model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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