Journal articles: 'Probabilité discrète'

1

Embrechts, Paul, and Hugh I. Gordon. "Discrete Probability." Journal of the American Statistical Association 93, no. 443 (1998): 1243. http://dx.doi.org/10.2307/2669882.

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PD, David Aldous, and James Propp. "Microsurveys in Discrete Probability." Journal of the American Statistical Association 94, no. 447 (1999): 989. http://dx.doi.org/10.2307/2670027.

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3

Krapavitskaite, D. "Discrete semistable probability distributions." Journal of Soviet Mathematics 38, no. 5 (1987): 2309–19. http://dx.doi.org/10.1007/bf01093832.

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Zhou, Shizhong, Liwei Liu, and Jianjun Li. "A Discrete-Time Queue with Preferred Customers and Partial Buffer Sharing." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/173938.

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We analyze a discrete-time Geo/Geo/1 queueing system with preferred customers and partial buffer sharing. In this model, customers arrive according to geometrical arrival processes with probabilityλ. If an arriving customer finds the server idle, he begins instantly his services. Otherwise, if the server is busy at the arrival epoch, the arrival either interrupts the customer being served to commence his own service with probabilityθ(the customer is called the preferred customer) or joins the waiting line at the back of the queue with probabilityθ~(the customer is called the normal customer) if permitted. The interrupted customer joins the waiting line at the head of the queue. If the total number of customers in the system is equal to or more than thresholdN, the normal customer will be ignored to enter into the system. But this restriction is not suitable for the preferred customers; that is, this system never loses preferred customers. A necessary and sufficient condition for the system to be stable is investigated and the stationary distribution of the queue length of the system is also obtained. Further, we develop a novel method to solve the probability generating function of the busy period of the system. The distribution of sojourn time of a customer in the server and the other indexes are acquired as well.

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Yang, Bill Huajian. "Monotonic Estimation for the Survival Probability over a Risk-Rated Portfolio by Discrete-Time Hazard Rate Models." International Journal of Machine Learning and Computing 9, no. 5 (2019): 675–81. http://dx.doi.org/10.18178/ijmlc.2019.9.5.857.

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LENCZEWSKI, ROMUALD, and RAFAŁ SAŁAPATA. "DISCRETE INTERPOLATION BETWEEN MONOTONE PROBABILITY AND FREE PROBABILITY." Infinite Dimensional Analysis, Quantum Probability and Related Topics 09, no. 01 (2006): 77–106. http://dx.doi.org/10.1142/s021902570600224x.

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We construct a sequence of states called m-monotone product states which give a discrete interpolation between the monotone product of states of Muraki in monotone probability and the free product of states of Avitzour and Voiculescu in free probability. We derive the associated basic limit theorems and develop the combinatorics based on non-crossing ordered partitions with monotone order starting from depth m. We deduce an explicit formula for the Cauchy transforms of the m-monotone central limit measures and for the associated Jacobi coefficients. A new type of combinatorics of inner blocks in non-crossing partitions leads to explicit formulas for the mixed moments of m-monotone Gaussian operators, which are new even in the case of monotone independent Gaussian operators with arcsine distributions.

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Ivanov, V. A., G. I. Ivchenko, and Yu I. Medvedev. "Discrete problems in probability theory." Journal of Soviet Mathematics 31, no. 2 (1985): 2759–95. http://dx.doi.org/10.1007/bf02116601.

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8

Tuden, Ivana Geček. "Ruin probability for discrete risk processes." Studia Scientiarum Mathematicarum Hungarica 56, no. 4 (2019): 420–39. http://dx.doi.org/10.1556/012.2019.56.4.1441.

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Abstract We study the discrete time risk process modelled by the skip-free random walk and derive results connected to the ruin probability and crossing a fixed level for this type of process. We use the method relying on the classical ballot theorems to derive the results for crossing a fixed level and compare them to the results known for the continuous time version of the risk process. We generalize this model by adding a perturbation and, still relying on the skip-free structure of that process, we generalize the previous results on crossing the fixed level for the generalized discrete time risk process. We further derive the famous Pollaczek-Khinchine type formula for this generalized process, using the decomposition of the supremum of the dual process at some special instants of time.

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9

Quine, M. P. "PROBABILITY APPROXIMATIONS FOR DIVISIBLE DISCRETE DISTRIBUTIONS." Australian Journal of Statistics 36, no. 3 (1994): 339–49. http://dx.doi.org/10.1111/j.1467-842x.1994.tb00886.x.

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Kupershmidt, Boris A. "q-Probability: I. Basic Discrete Distributions." Journal of Nonlinear Mathematical Physics 7, no. 1 (2000): 73–93. http://dx.doi.org/10.2991/jnmp.2000.7.1.6.

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Khromov, V. V., �. F. Kryuchkov, G. V. Tikhomirov, and L. A. Goncharov. "A probability method of discrete ordinates." Atomic Energy 73, no. 6 (1992): 933–38. http://dx.doi.org/10.1007/bf00761426.

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12

Maziero, Jonas. "Generating Pseudo-Random Discrete Probability Distributions." Brazilian Journal of Physics 45, no. 4 (2015): 377–82. http://dx.doi.org/10.1007/s13538-015-0337-8.

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13

Mielke, Paul W., Janis E. Johnston, and Kenneth J. Berry. "Combining Probability Values from Independent Permutation Tests: A Discrete Analog of Fisher's Classical Method." Psychological Reports 95, no. 2 (2004): 449–58. http://dx.doi.org/10.2466/pr0.95.2.449-458.

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Permutation tests are based on all possible arrangements of observed data sets. Consequently, such tests yield exact probability values obtained from discrete probability distributions. An exact nondirectional method to combine independent probability values that obey discrete probability distributions is introduced. The exact method is the discrete analog to Fisher's classical method for combining probability values from independent continuous probability distributions. If the combination of probability values includes even one probability value that obeys a sparse discrete probability distribution, then Fisher's classical method may be grossly inadequate.

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14

Hanzon, Bernard, and Raimund J. Ober. "State space calculations for discrete probability densities." Linear Algebra and its Applications 350, no. 1-3 (2002): 67–87. http://dx.doi.org/10.1016/s0024-3795(02)00268-9.

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15

Mõttus, Matti. "Photon recollision probability in discrete crown canopies." Remote Sensing of Environment 110, no. 2 (2007): 176–85. http://dx.doi.org/10.1016/j.rse.2007.02.015.

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16

Malvestuto, F. M. "Approximating discrete probability distributions with decomposable models." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 5 (1991): 1287–94. http://dx.doi.org/10.1109/21.120082.

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17

Kurth, Robert E., and David C. Cox. "Discrete Probability Distributions for Probabilistic Fracture Mechanics." Risk Analysis 5, no. 3 (1985): 235–40. http://dx.doi.org/10.1111/j.1539-6924.1985.tb00175.x.

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18

Nishimura, kazuo, and Masaaki sibuya. "Extended stirling family of discrete probability distributions." Communications in Statistics - Theory and Methods 26, no. 7 (1997): 1727–44. http://dx.doi.org/10.1080/03610929708832009.

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19

Wang, Mingjin. "A new discrete probability space with applications." Journal of Mathematical Analysis and Applications 455, no. 2 (2017): 1733–42. http://dx.doi.org/10.1016/j.jmaa.2017.06.069.

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20

Saad, Feras A., Cameron E. Freer, Martin C. Rinard, and Vikash K. Mansinghka. "Optimal approximate sampling from discrete probability distributions." Proceedings of the ACM on Programming Languages 4, POPL (2020): 1–31. http://dx.doi.org/10.1145/3371104.

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21

Rao, M. Bhaskara, Haimeng Zhang, Chunfeng Huang, and Fu-Chih Cheng. "A discrete probability problem in card shuffling." Communications in Statistics - Theory and Methods 45, no. 3 (2015): 612–20. http://dx.doi.org/10.1080/03610926.2013.834451.

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22

Czarna, Irmina, Zbigniew Palmowski, and Przemysław Świa̧tek. "Discrete time ruin probability with Parisian delay." Scandinavian Actuarial Journal 2017, no. 10 (2016): 854–69. http://dx.doi.org/10.1080/03461238.2016.1261734.

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23

Wiegand, Martin, Saralees Nadarajah, and Yuanyuan Zhang. "Discrete analogues of continuous multivariate probability distributions." Annals of Operations Research 292, no. 1 (2020): 183–90. http://dx.doi.org/10.1007/s10479-020-03633-5.

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24

Bertin, Emile, and Radu Theodorescu. "On the unimodality of discrete probability measures." Mathematische Zeitschrift 201, no. 1 (1989): 131–37. http://dx.doi.org/10.1007/bf01162000.

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25

Xiao, Sheng-xie, and En-lin Lü. "Mathematical expectation about discrete random variable with interval probability or fuzzy probability." Applied Mathematics and Mechanics 26, no. 10 (2005): 1382–90. http://dx.doi.org/10.1007/bf03246243.

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26

Bruno, William J., Gian-Carlo Rota, and David C. Torney. "Probability set functions." Annals of Combinatorics 3, no. 1 (1999): 13–25. http://dx.doi.org/10.1007/bf01609871.

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27

Malakar, Indra. "Theorizing Probability Distribution in Applied Statistics." Journal of Population and Development 1, no. 1 (2020): 79–95. http://dx.doi.org/10.3126/jpd.v1i1.33107.

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This paper investigates into theoretical knowledge on probability distribution and the application of binomial, poisson and normal distribution. Binomial distribution is widely used discrete random variable when the trails are repeated under identical condition for fixed number of times and when there are only two possible outcomes whereas poisson distribution is for discrete random variable for which the probability of occurrence of an event is small and the total number of possible cases is very large and normal distribution is limiting form of binomial distribution and used when the number of cases is infinitely large and probabilities of success and failure is almost equal.

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28

Jovanovic, Zoran, and Bratislav Dankovic. "On the probability stability of discrete-time control systems." Facta universitatis - series: Electronics and Energetics 17, no. 1 (2004): 11–20. http://dx.doi.org/10.2298/fuee0401011j.

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The problem of probability stability discrete-time control system is considered. A method for stability estimation of the arbitrary order systems is given. Probability stability discrete-time control systems with random parameters are also analyzed.

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29

Mielke, Paul W., Kenneth J. Berry, and Janis E. Johnston. "Comparisons of Continuous and Discrete Methods for Combining Probability Values Associated with Matched-Pairs t-Test Data." Perceptual and Motor Skills 100, no. 3 (2005): 799–805. http://dx.doi.org/10.2466/pms.100.3.799-805.

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Fisher's well-known continuous method for combining independent probability values from continuous distributions is compared with an exact discrete analog of Fisher's continuous method for combining independent probability values from discrete distributions using matched-pairs t-test data. Fisher's continuous method is shown to be inadequate for combining probability values from many discrete distributions, given the continuity assumption when discrete distributions are considered. Although Fisher's continuous method does not detect a well-documented effect among distributions, the exact discrete analog method clearly detects the effect.

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30

Eisenberg, Bennett, and B. K. Ghosh. "Independent Events in a Discrete Uniform Probability Space." American Statistician 41, no. 1 (1987): 52. http://dx.doi.org/10.2307/2684321.

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31

Tagliani, Aldo. "Discrete probability distributions in the generalized moment problem." Applied Mathematics and Computation 112, no. 2-3 (2000): 333–43. http://dx.doi.org/10.1016/s0096-3003(99)00062-4.

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32

Tagliani, Aldo. "Discrete probability distributions and moment problem: numerical aspects." Applied Mathematics and Computation 119, no. 1 (2001): 47–56. http://dx.doi.org/10.1016/s0096-3003(99)00228-3.

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33

Handley, John C., and Edward R. Dougherty. "Probability distributions for discrete one-dimensional coverage processes." Signal Processing 69, no. 2 (1998): 163–68. http://dx.doi.org/10.1016/s0165-1684(98)00101-7.

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34

Eisenberg, Bennett, and B. K. Ghosh. "Independent Events in a Discrete Uniform Probability Space." American Statistician 41, no. 1 (1987): 52–56. http://dx.doi.org/10.1080/00031305.1987.10475443.

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35

Rodríguez-Avi, J., A. Conde-Sánchez, A. J. Sáez-Castillo, and M. J. Olmo-Jiménez. "Gaussian Hypergeometric Probability Distributions for Fitting Discrete Data." Communications in Statistics - Theory and Methods 36, no. 3 (2007): 453–63. http://dx.doi.org/10.1080/03610920601001733.

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36

Fang, Yue. "Semi-Parametric Specification Tests for Discrete Probability Models." Journal of Risk & Insurance 70, no. 1 (2003): 73–84. http://dx.doi.org/10.1111/1539-6975.00048.

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37

Chhetry, Devendra, and Allan R. Sampson. "A Projection Decomposition for Bivariate Discrete Probability Distributions." SIAM Journal on Algebraic Discrete Methods 8, no. 3 (1987): 501–9. http://dx.doi.org/10.1137/0608041.

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38

SAKAMOTO, Jun, Yasuhiro MORI, and Takayoshi SEKIOKA. "PROBABILITY ANALYSIS METHOD BY DISCRETE FAST FOURIER TRANSFORM." Journal of Structural and Construction Engineering (Transactions of AIJ) 60, no. 472 (1995): 39–45. http://dx.doi.org/10.3130/aijs.60.39_2.

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39

Arinkin, D., та A. Borodin. "τ-function of discrete isomonodromy transformations and probability". Compositio Mathematica 145, № 03 (2009): 747–72. http://dx.doi.org/10.1112/s0010437x08003862.

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AbstractWe introduce theτ-function of a difference rational connection (d-connection) and its isomonodromy transformations. We show that in a continuous limit ourτ-function agrees with the Jimbo–Miwa–Uenoτ-function. We compute theτ-function for the isomonodromy transformations leading to difference Painlevé V and difference Painlevé VI equations. We prove that the gap probability for a wide class of discrete random matrix type models can be viewed as theτ-function for an associated d-connection.

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40

Weigel, Andreas P., Mark A. Liniger, and Christof Appenzeller. "The Discrete Brier and Ranked Probability Skill Scores." Monthly Weather Review 135, no. 1 (2007): 118–24. http://dx.doi.org/10.1175/mwr3280.1.

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Abstract The Brier skill score (BSS) and the ranked probability skill score (RPSS) are widely used measures to describe the quality of categorical probabilistic forecasts. They quantify the extent to which a forecast strategy improves predictions with respect to a (usually climatological) reference forecast. The BSS can thereby be regarded as the special case of an RPSS with two forecast categories. From the work of Müller et al., it is known that the RPSS is negatively biased for ensemble prediction systems with small ensemble sizes, and that a debiased version, the RPSSD, can be obtained quasi empirically by random resampling from the reference forecast. In this paper, an analytical formula is derived to directly calculate the RPSS bias correction for any ensemble size and combination of probability categories, thus allowing an easy implementation of the RPSSD. The correction term itself is identified as the “intrinsic unreliability” of the ensemble prediction system. The performance of this new formulation of the RPSSD is illustrated in two examples. First, it is applied to a synthetic random white noise climate, and then, using the ECMWF Seasonal Forecast System 2, to seasonal predictions of near-surface temperature in several regions of different predictability. In both examples, the skill score is independent of ensemble size while the associated confidence thresholds decrease as the number of ensemble members and forecast/observation pairs increase.

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41

McKeown†, G. P., and V. J. Rayward‐Smith. "Discrete and continuous probability for the computer scientist." International Journal of Mathematical Education in Science and Technology 19, no. 1 (1988): 41–56. http://dx.doi.org/10.1080/0020739880190104.

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42

Butt, Nabeel. "On Discrete Probability Approximations for Transaction Cost Problems." Asia-Pacific Financial Markets 26, no. 3 (2019): 365–89. http://dx.doi.org/10.1007/s10690-019-09270-8.

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43

Bourgain, Jean, Van H. Vu, and Philip Matchett Wood. "On the singularity probability of discrete random matrices." Journal of Functional Analysis 258, no. 2 (2010): 559–603. http://dx.doi.org/10.1016/j.jfa.2009.04.016.

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44

Lebedev, A. V. "Correlation Approach to Studying Dependent Discrete Probability Spaces." Moscow University Mathematics Bulletin 76, no. 1 (2021): 9–15. http://dx.doi.org/10.3103/s0027132221010046.

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45

Harańczyk, Grzegorz, Wojciech Słomczyński, and Tomasz Zastawniak. "Relative and Discrete Utility Maximising Entropy." Open Systems & Information Dynamics 15, no. 04 (2008): 303–27. http://dx.doi.org/10.1142/s1230161208000213.

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The notion of utility maximising entropy (u-entropy) of a probability density, which was introduced and studied in [37], is extended in two directions. First, the relative u-entropy of two probability measures in arbitrary probability spaces is defined. Then, specialising to discrete probability spaces, we also introduce the absolute u-entropy of a probability measure. Both notions are based on the idea, borrowed from mathematical finance, of maximising the expected utility of the terminal wealth of an investor. Moreover, u-entropy is also relevant in thermodynamics, as it can replace the standard Boltzmann-Shannon entropy in the Second Law. If the utility function is logarithmic or isoelastic (a power function), then the well-known notions of Boltzmann-Shannon and Rényi relative entropy are recovered. We establish the principal properties of relative and discrete u-entropy and discuss the links with several related approaches in the literature.

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46

Foote, Mike. "Estimating taxonomic durations and preservation probability." Paleobiology 23, no. 3 (1997): 278–300. http://dx.doi.org/10.1017/s0094837300019692.

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Paleontological completeness and stratigraphic ranges depend on extinction rate, origination rate, preservation rate, and the length of the interval of time over which observations can be made. I derive expressions for completeness and the distribution of durations and ranges as functions of these parameters, considering both continuous- and discrete-time models.Previous work has shown that, if stratigraphic ranges can be followed indefinitely forward, and if extinction and preservation occur at stochastically constant rates, then extinction rate and preservability can be estimated from (1) discrete (binned) stratigraphic ranges even if data on occurrences within ranges are unknown, and (2) continuous ranges if the number of occurrences within each range is known. I show that, regardless of whether the window of observation is finite or infinite, extinction and preservation rates can also be estimated from (3) continuous ranges when the number of occurrences is not known, and (4) discrete ranges when the number of occurrences is not known. One previous estimation method for binned data involves a sample-size bias. This is circumvented by using maximum likelihood parameter estimation. It is worth exploiting data on occurrences within ranges when these are available, since they allow preservation rate to be estimated with less variance. The various methods yield comparable parameter estimates when applied to Cambro-Ordovician trilobite species and Cenozoic mammal species.Stratigraphic gaps and variable preservation affect stratigraphic ranges predictably. In many cases, accurate parameter estimation is possible even in the face of these complications. The distribution of stratigraphic ranges can be used to estimate the sizes of gaps if their existence is known.

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47

Wang, Yaming, Deming Shen, Wenqing Huang, and Yonghua Han. "Three-dimensional nonrigid reconstruction based on probability model." International Journal of Advanced Robotic Systems 17, no. 1 (2020): 172988142090162. http://dx.doi.org/10.1177/1729881420901627.

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Most nonrigid motions use shape-based methods to solve the problem; however, the use of discrete cosine transform trajectory-based methods to solve the nonrigid motion problem is also very prominent. The signal undergoes discrete transformation due to the transform characteristics of the discrete cosine transform. The correlation of the data is well extracted such that a better compression of data is achieved. However, it is important to select the number and sequence of discrete cosine transform trajectory basis appropriately. The error of reconstruction and operational costs will increase for a high value of K (number of trajectory basis). On the other hand, a lower value of K would lead to the exclusion of information components. This will lead to poor accuracy as the structure of the object cannot be fully represented. When the number of trajectory basis is determined, the combination form has a considerable influence on the reconstruction algorithm. This article selects an appropriate number and combination of trajectory basis by analyzing the spectrum of re-projection errors and realizes the automatic selection of trajectory basis. Then, combining with the probability framework of normal distribution of a low-order model matrix, the energy information of the high-frequency part is retained, which not only helps maintain accuracy but also improves reconstruction efficiency. The proposed method can be used to reconstruct the three-dimensional structure of sparse data under more precise prior conditions and lower computational costs.

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48

Bidabad, Bijan, and Behrouz Bidabad. "Complex Probability and Markov Stochastic Process." Indian Journal of Finance and Banking 3, no. 1 (2019): 13–22. http://dx.doi.org/10.46281/ijfb.v3i1.290.

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This note discusses the existence of "complex probability" in the real world sensible problems. By defining a measure more general than the conventional definition of probability, the transition probability matrix of discrete Markov chain is broken to the periods shorter than a complete step of the transition. In this regard, the complex probability is implied.

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Krishna, Hare, and Pramendra Singh Pundir. "Discrete Burr and discrete Pareto distributions." Statistical Methodology 6, no. 2 (2009): 177–88. http://dx.doi.org/10.1016/j.stamet.2008.07.001.

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50

COLLINS, Benoit, Takahiro HASEBE, and Noriyoshi SAKUMA. "Free probability for purely discrete eigenvalues of random matrices." Journal of the Mathematical Society of Japan 70, no. 3 (2018): 1111–50. http://dx.doi.org/10.2969/jmsj/77147714.

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Journal articles on the topic 'Probabilité discrète'

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