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Journal articles on the topic 'Probability of error'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Noga, Krystyna M. "Error probability and error stream properties in channel with slow Rician fading." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2003): 3–8. http://dx.doi.org/10.26636/jtit.2003.4.208.

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In a radio communication channel wave parameters fluctuate randomly. The signal envelope undergoes deep fades. When binary information is transmitted through such a channel, fading causes random variation of probabilities of error associated with the detection of individual elementary signals, which produces a clustering of errors. The paper presents an analytical description of the probability of bit error in the channel with very slow Rician fading and Gaussian noise for noncoherent and coherent detection. Digital systems employing error detection or error correction coding are generally bas

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2

Lao, D., and A. M. Haimovich. "Symbol-Error Probability and Bit-Error Probability for Optimum Combining With MPSK Modulation." IEEE Transactions on Communications 52, no. 8 (2004): 1276–81. http://dx.doi.org/10.1109/tcomm.2004.833040.

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IKEUCHI, Takashi, Terumoto KOMORI, Yoshihiko NOMURA, Hirokazu MATSUI, and Norihiko KATO. "Error Analysis Bayse' a Posteriori Probability Error Propagated from Data Error." Transactions of the Japan Society of Mechanical Engineers Series C 67, no. 656 (2001): 1092–98. http://dx.doi.org/10.1299/kikaic.67.1092.

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4

Pelc, Andrzej. "Searching with known error probability." Theoretical Computer Science 63, no. 2 (1989): 185–202. http://dx.doi.org/10.1016/0304-3975(89)90077-7.

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5

Nadarajah, Saralees, and Samuel Kotz. "Expressions for bit error probability." Wireless Communications and Mobile Computing 8, no. 7 (2008): 885–94. http://dx.doi.org/10.1002/wcm.535.

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6

Sudhanshu, Aggarwal*1 &. Swarg Deep Sharma2. "SADIK TRANSFORM OF ERROR FUNCTION (PROBABILITY INTEGRAL)." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 6 (2019): 125–35. https://doi.org/10.5281/zenodo.3250247.

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The solutions of many advanced engineering problems like Fick’s second law, heat and mass transfer problems, vibrating beams problems contains error and complementary error function. When we use any integral transform to solve these types of problems, it is very necessary to know the integral transform of error function. In this article, we find the Sadik transform of error and complementary error functions. To demonstrate the usefulness of Sadik transform of error function, some numerical applications are considered in application section for solving improper integrals which contain err

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7

Schiller, Frank, Dan Judd, Peerasan Supavatanakul, Tina Hardt, and Felix Wieczorek. "Enhancement of safety communication model." at - Automatisierungstechnik 70, no. 1 (2022): 38–52. http://dx.doi.org/10.1515/auto-2021-0098.

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Abstract A fundamental measure of safety communication is the residual error probability, i. e., the probability of undetected errors. For the detection of data errors, typically a Cyclic Redundancy Check (CRC) is applied, and the resulting residual error probability is determined based on the Binary Symmetric Channel (BSC) model. The use of this model had been questioned since several error types cannot be sufficiently described. Especially the increasing introduction of security algorithms into underlying communication layers requires a more adequate channel model. This paper introduces an e

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IKEUCHI, Takashi, Terumoto KOMORI, Yoshihiko NOMURA, Hirokazu MATSUI, and Norihiko KATO. "Error Analysis on Bayse' a posteriori probability error propagated from data error." Proceedings of the JSME annual meeting 2000.1 (2000): 243–44. http://dx.doi.org/10.1299/jsmemecjo.2000.1.0_243.

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9

Cheng, Qiang, Can Wu, Peihua Gu, Wenfen Chang, and Dongsheng Xuan. "An Analysis Methodology for Stochastic Characteristic of Volumetric Error in Multiaxis CNC Machine Tool." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/863283.

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Traditional approaches about error modeling and analysis of machine tool few consider the probability characteristics of the geometric error and volumetric error systematically. However, the individual geometric error measured at different points is variational and stochastic, and therefore the resultant volumetric error is aslo stochastic and uncertain. In order to address the stochastic characteristic of the volumetric error for multiaxis machine tool, a new probability analysis mathematical model of volumetric error is proposed in this paper. According to multibody system theory, a mean val

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10

Aguirre, Alejandro, Philipp G. Haselwarter, Markus de Medeiros, et al. "Error Credits: Resourceful Reasoning about Error Bounds for Higher-Order Probabilistic Programs." Proceedings of the ACM on Programming Languages 8, ICFP (2024): 284–316. http://dx.doi.org/10.1145/3674635.

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Probabilistic programs often trade accuracy for efficiency, and thus may, with a small probability, return an incorrect result. It is important to obtain precise bounds for the probability of these errors, but existing verification approaches have limitations that lead to error probability bounds that are excessively coarse, or only apply to first-order programs. In this paper we present Eris, a higher-order separation logic for proving error probability bounds for probabilistic programs written in an expressive higher-order language. Our key novelty is the introduction of error credits, a sep

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11

Dance, Sandy, Elizabeth Ebert, and David Scurrah. "Thunderstorm Strike Probability Nowcasting." Journal of Atmospheric and Oceanic Technology 27, no. 1 (2010): 79–93. http://dx.doi.org/10.1175/2009jtecha1279.1.

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Abstract To assist in thunderstorm warning, automated nowcasting systems have been developed that detect thunderstorm cells in radar images and propagate them forward in time to generate forecasted threat areas. Current methods, however, fail to quantify the probabilistic nature of the error structure of such forecasts. This paper introduces the Thunderstorm Environment Strike Probability Algorithm (THESPA), which forecasters can use to provide probabilistic thunderstorm nowcasts for risk assessment and emergency decision making. This method accounts for the prediction error by transforming th

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12

Wang, Yuan, Jiaheng Wang, Derrick Wing Kwan Ng, Robert Schober, and Xiqi Gao. "A Minimum Error Probability NOMA Design." IEEE Transactions on Wireless Communications 20, no. 7 (2021): 4221–37. http://dx.doi.org/10.1109/twc.2021.3056597.

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13

Feder, M., and N. Merhav. "Relations between entropy and error probability." IEEE Transactions on Information Theory 40, no. 1 (1994): 259–66. http://dx.doi.org/10.1109/18.272494.

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14

Rugini, Luca. "Symbol Error Probability of Hexagonal QAM." IEEE Communications Letters 20, no. 8 (2016): 1523–26. http://dx.doi.org/10.1109/lcomm.2016.2574343.

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15

Vasconcelos, N. "Minimum Probability of Error Image Retrieval." IEEE Transactions on Signal Processing 52, no. 8 (2004): 2322–36. http://dx.doi.org/10.1109/tsp.2004.831125.

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16

Liu, Bin, Biao Chen, and Rick S. Blum. "Minimum Error Probability Cooperative Relay Design." IEEE Transactions on Signal Processing 55, no. 2 (2007): 656–64. http://dx.doi.org/10.1109/tsp.2006.885778.

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17

Mahlab, Uri, Michael Fleisher, and Joseph Shamir. "Error probability in optical pattern recognition." Optics Communications 77, no. 5-6 (1990): 415–22. http://dx.doi.org/10.1016/0030-4018(90)90137-i.

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18

Campi, Marco C. "Classification with guaranteed probability of error." Machine Learning 80, no. 1 (2010): 63–84. http://dx.doi.org/10.1007/s10994-010-5183-x.

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19

Dupraz, Elsa, David Declercq, and Bane Vasic. "Asymptotic Error Probability of the Gallager B Decoder Under Timing Errors." IEEE Communications Letters 21, no. 4 (2017): 698–701. http://dx.doi.org/10.1109/lcomm.2017.2647804.

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20

Guo, Yundong, and Xinshi Suo. "Flight safety assessment based on a modified human error risk quantification approach." PLOS ONE 19, no. 4 (2024): e0302511. http://dx.doi.org/10.1371/journal.pone.0302511.

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In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error dat

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21

Zhelev, Stefan. "Comparative analysis of the probability in digital wireless communication systems." Journal scientific and applied research 2, no. 1 (2012): 21–27. http://dx.doi.org/10.46687/jsar.v2i1.38.

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Digital signals dominate satellite communications systems. A measure the quality of digital signals is Bit Error Rate (BER) and Symbol Error Rate (SER). The main purpose of the paper is a comparison of the probability of error for various modulation and demodulation schemes used in digital communications systems (DCS) for transmitting information over channel. Error Rate parameters are resulting analytically, not experimentally.

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22

Hirota, Osamu. "Introduction to Semi-Classical Analysis for Digital Errors of Qubit in Quantum Processor." Entropy 23, no. 12 (2021): 1577. http://dx.doi.org/10.3390/e23121577.

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In recent years, remarkable progress has been achieved in the development of quantum computers. For further development, it is important to clarify properties of errors by quantum noise and environment noise. However, when the system scale of quantum processors is expanded, it has been pointed out that a new type of quantum error, such as nonlinear error, appears. It is not clear how to handle such new effects in information theory. First of all, one should make the characteristics of the error probability of qubits clear as communication channel error models in information theory. The purpose

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23

Liu, Zhao Xia, and Lian Jun Chen. "Post-Accident Human Reliability Analysis and Control of Mine Hoisting System." Advanced Materials Research 616-618 (December 2012): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.461.

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Using the method of THERPand HCR this paper studies post- accident human error events of mine hoisting system which reflects accident consequence seriousness and accident treatment urgency. It ascertains cognitive failure probability P1, non-response probability P2 and the failure probability P3, and quantities and appraises degree of human reliability. Finally this paper analyzes causes of hoisting accident human errors ,by which probability human error can be reduced to the lowest limit.

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24

Wang, Zhi-qiang, Dan Ding, and Peng-yu Tang. "Parallel Combined Spread Spectrum Error Analysis And Succesive Error Correction Method." Journal of Physics: Conference Series 2378, no. 1 (2022): 012081. http://dx.doi.org/10.1088/1742-6596/2378/1/012081.

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Abstract Traditional spread spectrum techniques provide a strong anti-interference capability but a relatively low transmission rate. This is addressed by the parallel combination spread spectrum technique which provides higher transmission efficiency, security, and spread gain. Nevertheless, the receiver of a parallel combined spread spectrum system requires to distinguish multiple spread spectrum sequences. Therefore, any reception errors are propagated in the receiver hence leading to a larger number of errors. Moreover, spread spectrum sequences with different mode values experience differ

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25

Grimit, Eric P., and Clifford F. Mass. "Measuring the Ensemble Spread–Error Relationship with a Probabilistic Approach: Stochastic Ensemble Results." Monthly Weather Review 135, no. 1 (2007): 203–21. http://dx.doi.org/10.1175/mwr3262.1.

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Abstract One widely accepted measure of the utility of ensemble prediction systems is the relationship between ensemble spread and deterministic forecast accuracy. Unfortunately, this relationship is often characterized by spread–error linear correlations, which oversimplify the true spread–error relationship and ignore the possibility that some end users have categorical sensitivities to forecast error. In the present paper, a simulation study is undertaken to estimate the idealized spread–error statistics for stochastic ensemble prediction systems of a finite size. Under a variety of spread–

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26

Sterkens, Philippe, Ralf Caers, Marijke De Couck, Victor Van Driessche, Michael Geamanu, and Stijn Baert. "Costly mistakes: Why and when spelling errors in resumes jeopardise interview chances." PLOS ONE 18, no. 4 (2023): e0283280. http://dx.doi.org/10.1371/journal.pone.0283280.

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The analysis of hiring penalties due to spelling errors has been restricted to white-collar occupations and error-laden resumes. Moreover, the mechanisms underlying these penalties remained unclear. To fill these gaps, we conducted a scenario experiment with 445 recruiters. Compared to error-free resumes, hiring penalties are inflicted for error-laden resumes (18.5 percent points lower interview probability) and resumes with fewer errors (7.3 percent points lower interview probability). Furthermore, we find heterogeneity in penalties inflicted. Half of the penalty can be explained by the perce

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27

Ong, C. T., and C. Leung. "On the undetected error probability of triple-error-correcting BCH codes." IEEE Transactions on Information Theory 37, no. 3 (1991): 673–78. http://dx.doi.org/10.1109/18.79930.

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28

Bliadze, A., and V. Gurevich. "Equivalent to the Probability of Error with Block Error Correction Codes." Telecom IT 7, no. 3 (2019): 1–6. http://dx.doi.org/10.31854/2307-1303-2019-7-3-1-6.

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The probabilities of bit and code errors are compared when using non-redundant and noise-resistant block codes in a digital radio system for transmitting information. The analysis results are accompanied by graphs that allow you to select the bit rate of the noise-tolerant code necessary to ensure the specified probability of code errors.

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29

Castilla-Earls, Anny, David J. Francis, and Aquiles Iglesias. "The Complex Role of Utterance Length on Grammaticality: Multivariate Multilevel Analysis of English and Spanish Utterances of First-Grade English Learners." Journal of Speech, Language, and Hearing Research 65, no. 1 (2022): 238–52. http://dx.doi.org/10.1044/2021_jslhr-20-00464.

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Purpose: This study examined the relationship between utterance length, syntactic complexity, and the probability of making an error at the utterance level. Method: The participants in this study included 830 Spanish-speaking first graders who were learning English at school. Story retells in both Spanish and English were collected from all children. Generalized mixed linear models were used to examine within-child and between-children effects of utterance length and subordination on the probability of making an error at the utterance level. Results: The relationship between utterance length a

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30

Flannelly, Laura T., Kevin J. Flannelly, and Malcolm S. McLeod. "Comparison of Forced-Choice and Subjective Probability Scales Measuring Behavioral Intentions." Psychological Reports 86, no. 1 (2000): 321–32. http://dx.doi.org/10.2466/pr0.2000.86.1.321.

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Three surveys compared the accuracy of predictions based on forced-choice and subjective probability scales. The latter produced significantly more accurate election predictions and significantly reduced the percentage of undecided, or “Don't Know” responses, compared to forced-choice scales in all three surveys. Analysis indicates subjective probability scales decrease sampling error and confirms there is an inherent source of error in traditional forced-choice questions about voting intentions not attributable to sampling error. The results are discussed with respect to (1) sampling and meas

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31

Napp, John, and John Preskill. "Optimal Bacon-Shor codes." Quantum Information and Computation 13, no. 5&6 (2013): 490–510. http://dx.doi.org/10.26421/qic13.5-6-9.

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We study the performance of Bacon-Shor codes, quantum subsystem codes which are well suited for applications to fault-tolerant quantum memory because the error syndrome can be extracted by performing two-qubit measurements. Assuming independent noise, we find the optimal block size in terms of the bit-flip error probability $p_X$ and the phase error probability $p_Z$, and determine how the probability of a logical error depends on $p_X$ and $p_Z$. We show that a single Bacon-Shor code block, used by itself without concatenation, can provide very effective protection against logical errors if t

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32

Kim, Namyong. "Estimation of Zero-Error Probability of Constant Modulus Errors for Blind Equalization." Journal of Internet Computing and Services 15, no. 5 (2014): 17–24. http://dx.doi.org/10.7472/jksii.2014.15.5.17.

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33

Triliana, T., and E. C. M. Asih. "Analysis of students’ errors in solving probability based on Newman’s error analysis." Journal of Physics: Conference Series 1211 (April 2019): 012061. http://dx.doi.org/10.1088/1742-6596/1211/1/012061.

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34

Du, Xiang Ran, Hai Tao Liu, and Min Zhang. "Comparative Analysis on Kernel Based Probability Density Estimation." Applied Mechanics and Materials 543-547 (March 2014): 1655–58. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1655.

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In this paper, we compare the estimation performances of 7 different kernels (i.e., Uniform, Triangular, Epanechnikov, Biweight, Triweight, Cosine and Gaussian) when using them to conduct the probability density estimation with Parzen window method. We firstly analyze the efficiencies of these 7 kernels and then compare their estimation errors measured by mean squared error (MSE). The theoretical analysis and the experimental comparisons show that the mostly-used Gaussian kernel is not the best choice for the probability density estimation, of which the efficiency is low and estimation error i

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35

Podoltsev, V. V. "Estimation of the probabilistic characteristics of the majority method for processing short segments of a pseudo-random sequence." Radio industry (Russia) 30, no. 1 (2020): 8–15. http://dx.doi.org/10.21778/2413-9599-2020-30-1-8-15.

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Simulation modeling of the processing method for pseudo-random sequence (PRS) short segments based on the majority tests gives the probability characteristics that are different from the results of a calculation using the Gaussian approximation. The simulation modeling shows that the probability of bit error decoding is changed discretely with the explicit minima and maxima for the length of the processed segment of the PRS. This is due to the fact that the decoder always uses a fixed test sequence, which is determined by the initial phase of the analyzer. Thus, the distribution of these check

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36

Soetedjo, Robijanto, Yoshiko Kojima, and Albert F. Fuchs. "Complex Spike Activity in the Oculomotor Vermis of the Cerebellum: A Vectorial Error Signal for Saccade Motor Learning?" Journal of Neurophysiology 100, no. 4 (2008): 1949–66. http://dx.doi.org/10.1152/jn.90526.2008.

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Brain stem signals that generate saccadic eye movements originate in the superior colliculus. They reach the pontine burst generator for horizontal saccades via short-latency pathways and a longer pathway through the oculomotor vermis (OMV) of the cerebellum. Lesion studies implicate the OMV in the adaptation of saccade amplitude that occurs when saccades become inaccurate because of extraocular muscle weakness or behavioral manipulations. We studied the nature of the possible error signal that might drive adaptation by examining the complex spike (CS) activity of vermis Purkinje (P-) cells in

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37

Baranov, A. P., and P. A. Baranov. "CRC Error Probability under Batch Random Noise." Automatic Control and Computer Sciences 52, no. 8 (2018): 1049–58. http://dx.doi.org/10.3103/s0146411618080047.

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38

Gillis, J. T. "Computation of the circular error probability integral." IEEE Transactions on Aerospace and Electronic Systems 27, no. 6 (1991): 906–10. http://dx.doi.org/10.1109/7.104258.

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39

Poor, H. V., and S. Verdu. "Probability of error in MMSE multiuser detection." IEEE Transactions on Information Theory 43, no. 3 (1997): 858–71. http://dx.doi.org/10.1109/18.568697.

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40

Feistauerova, J., and I. Vajda. "Testing system entropy and prediction error probability." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 5 (1993): 1352–58. http://dx.doi.org/10.1109/21.260666.

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41

Pooi Yuen Kam, Kwai Yin Seek, Tjeng Thiang Tjhung, and Pranesh Sinha. "Error probability of 2DPSK with phase noise." IEEE Transactions on Communications 42, no. 7 (1994): 2366–69. http://dx.doi.org/10.1109/26.297838.

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42

Schlegel, C. "Error probability calculation for multibeam Rayleigh channels." IEEE Transactions on Communications 44, no. 3 (1996): 290–93. http://dx.doi.org/10.1109/26.486321.

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43

Sheen, W. H., and G. L. Stuber. "Error probability for reduced-state sequence estimation." IEEE Journal on Selected Areas in Communications 10, no. 3 (1992): 571–78. http://dx.doi.org/10.1109/49.127779.

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44

Mouchtak, Yassine, and Faissal El Bouanani. "New Accurate Approximation for Average Error Probability." IEEE Access 9 (2021): 4388–97. http://dx.doi.org/10.1109/access.2020.3048130.

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45

Zalman, Gady, and Joseph Shamir. "Reducing error probability in optical pattern recognition." Journal of the Optical Society of America A 8, no. 12 (1991): 1866. http://dx.doi.org/10.1364/josaa.8.001866.

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46

Taylor-Adams, Sally E. "CORE-DATA, A Human Error Probability Database." Safety and Reliability 13, no. 4 (1993): 6–16. http://dx.doi.org/10.1080/09617353.1993.11690625.

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47

Kazakos, D. "Bayes error probability with inaccurate decision threshold." Electronics Letters 25, no. 14 (1989): 894. http://dx.doi.org/10.1049/el:19890599.

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48

Due., G., and HU Backer. "Pre-test and post-test error probability." Acta Paediatrica 85, no. 5 (1996): 542. http://dx.doi.org/10.1111/j.1651-2227.1996.tb14082.x.

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49

Basra, Gurpreet, and Barry Kirwan. "Collection of offshore human error probability data." Reliability Engineering & System Safety 61, no. 1-2 (1998): 77–93. http://dx.doi.org/10.1016/s0951-8320(97)00064-1.

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McCaffrey, D. F., J. R. Lockwood, and C. M. Setodji. "Inverse probability weighting with error-prone covariates." Biometrika 100, no. 3 (2013): 671–80. http://dx.doi.org/10.1093/biomet/ast022.

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