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Journal articles on the topic 'Processi random'

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Author: Grafiati

Published: 11 March 2023

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1

Kouznetsov, Dimitrii, and Valerii Voitsekhovich. "Die Methode der zufälligen Wellenvektoren zur Simulation von korrelierten Zufallsprozessen." Meteorologische Zeitschrift 7, no. 5 (November 2, 1998): 230–36. http://dx.doi.org/10.1127/metz/7/1998/230.

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2

FRYZ, Mykhailo, and Bogdana MLYNKO. "DISCRETE-TIME CONDITIONAL LINEAR RANDOM PROCESSES AND THEIR PROPERTIES." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (May 26, 2022): 7–12. http://dx.doi.org/10.31891/2307-5732-2022-309-3-7-12.

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Continuous-time conditional linear random process is represented as a stochastic integral of a random kernel driven by a process with independent increments. Such processes are used in the problems of mathematical modelling, computer simulation, and processing of stochastic signals, the physical nature of which generates them to be represented as the sum of many random impulses that occur at Poisson moments. Impulses are stochastically dependent functions, in contrast to another well-known mathematical model which is a linear random process, that has a similar structure but is represented as the sum of a large amount of independent random impulses that occur at Poisson moments of time. The application areas of these models are mathematical modelling, computer simulation, and processing of electroencephalographic signals, cardio signals, resource consumption processes (such as electricity consumption, water consumption, gas consumption), radar signals, etc. A discrete-time conditional linear random process has been defined in the paper, the relationships with corresponding continuous-time model has been shown. According to the given definition the discrete-time conditional linear random process can be considered as an output of linear digital filter with random parameters on the input of the white noise which is infinitely divisible distributed. Moment functions of first and second order have been analyzed. In particular, the expressions for mathematical expectation, variance and covariance function have been obtained. The results can be utilized to study the probabilistic characteristics of the investigated information stochastic signals, which will depend on the properties of the corresponding kernel and white noise. In particular, the conditions for the process to be wide-sense stationary have been represented.

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3

Xu, Hongzhi, Chunping Li, Li Li, and Hongyu Shi. "Accelerating the Training Process of Support Vector Machines by Random Partition." International Journal of Computer Theory and Engineering 7, no. 1 (February 2014): 29–33. http://dx.doi.org/10.7763/ijcte.2015.v7.925.

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4

Baddeley, A. J., and L. M. Cruz-Orive. "The Rao–Blackwell theorem in stereology and some counterexamples." Advances in Applied Probability 27, no. 01 (March 1995): 2–19. http://dx.doi.org/10.1017/s0001867800046188.

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A version of the Rao–Blackwell theorem is shown to apply to most, but not all, stereological sampling designs. Estimators based on random test grids typically have larger variance than quadrat estimators; randoms-dimensional samples are worse than randomr-dimensional samples fors < r.Furthermore, the standard stereological ratio estimators of different dimensions are canonically related to each other by the Rao–Blackwell process. However, there are realistic cases where sampling with a lower-dimensional probeincreasesefficiency. For example, estimators based on (conditionally) non-randomised test point grids may have smaller variance than quadrat estimators. Relative efficiency is related to issues in geostatistics and the theory of wide-sense stationary random fields. A uniform minimum variance unbiased estimator typically does not exist in our context.

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5

Duong, Dam Ton, and Hao Ngoc Duong. "ITÔ – HERMITE RANDOM PROCESS." Science and Technology Development Journal 13, no. 3 (September 30, 2010): 13–18. http://dx.doi.org/10.32508/stdj.v13i3.2149.

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6

Chung, Jaeyoung, Dohan Kim, and Eun Gu Lee. "Stationary hyperfunctional random process." Complex Variables and Elliptic Equations 59, no. 11 (March 14, 2013): 1547–58. http://dx.doi.org/10.1080/17476933.2012.757309.

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7

Korándi, Dániel, Yuval Peled, and Benny Sudakov. "A Random Triadic Process." SIAM Journal on Discrete Mathematics 30, no. 1 (January 2016): 1–19. http://dx.doi.org/10.1137/15m1012487.

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8

Schulman, Leonard J. "A random stacking process." Discrete Mathematics 257, no. 2-3 (November 2002): 541–47. http://dx.doi.org/10.1016/s0012-365x(02)00512-5.

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9

Sibuya, Masaaki. "A random clustering process." Annals of the Institute of Statistical Mathematics 45, no. 3 (1993): 459–65. http://dx.doi.org/10.1007/bf00773348.

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10

Welsh, D. J. A. "The random cluster process." Discrete Mathematics 136, no. 1-3 (December 1994): 373–90. http://dx.doi.org/10.1016/0012-365x(94)00120-8.

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11

Zhao, Ruiqing, Wansheng Tang, and Huaili Yun. "Random fuzzy renewal process." European Journal of Operational Research 169, no. 1 (February 2006): 189–201. http://dx.doi.org/10.1016/j.ejor.2004.04.049.

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12

Bloznelis, Mindaugas, and Michał Karoński. "Random Intersection Graph Process." Internet Mathematics 11, no. 4-5 (November 14, 2014): 385–402. http://dx.doi.org/10.1080/15427951.2014.982310.

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13

Korándi, Dániel, Yuval Peled, and Benny Sudakov. "A random triadic process." Electronic Notes in Discrete Mathematics 49 (November 2015): 189–96. http://dx.doi.org/10.1016/j.endm.2015.06.028.

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14

Ben‐Eliezer, Omri, Dan Hefetz, Gal Kronenberg, Olaf Parczyk, Clara Shikhelman, and Miloš Stojaković. "Semi‐random graph process." Random Structures & Algorithms 56, no. 3 (May 2020): 648–75. http://dx.doi.org/10.1002/rsa.20887.

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15

Cooper, Colin, Alan Frieze, and Juan Vera. "Random Deletion in a Scale-Free Random Graph Process." Internet Mathematics 1, no. 4 (January 2004): 463–83. http://dx.doi.org/10.1080/15427951.2004.10129095.

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16

Zhong, Guoqiang, Wu-Jun Li, Dit-Yan Yeung, Xinwen Hou, and Cheng-Lin Liu. "Gaussian Process Latent Random Field." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 679–84. http://dx.doi.org/10.1609/aaai.v24i1.7697.

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In this paper, we propose a novel supervised extension of GPLVM, called Gaussian process latent random field (GPLRF), by enforcing the latent variables to be a Gaussian Markov random field with respect to a graph constructed from the supervisory information.

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17

KRIVELEVICH, MICHAEL, BENNY SUDAKOV, and DAN VILENCHIK. "On the Random Satisfiable Process." Combinatorics, Probability and Computing 18, no. 5 (September 2009): 775–801. http://dx.doi.org/10.1017/s0963548309990356.

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In this work we suggest a new model for generating random satisfiable k-CNF formulas. To generate such formulas. randomly permute all $2^k\binom{n}{k}$ possible clauses over the variables x1,. . .,xn, and starting from the empty formula, go over the clauses one by one, including each new clause as you go along if, after its addition, the formula remains satisfiable. We study the evolution of this process, namely the distribution over formulas obtained after scanning through the first m clauses (in the random permutation's order).Random processes with conditioning on a certain property being respected are widely studied in the context of graph properties. This study was pioneered by Ruciński and Wormald in 1992 for graphs with a fixed degree sequence, and also by Erdős, Suen and Winkler in 1995 for triangle-free and bipartite graphs. Since then many other graph properties have been studied, such as planarity and H-freeness. Thus our model is a natural extension of this approach to the satisfiability setting.Our main contribution is as follows. For m ≥ cn, c = c(k) a sufficiently large constant, we are able to characterize the structure of the solution space of a typical formula in this distribution. Specifically, we show that typically all satisfying assignments are essentially clustered in one cluster, and all but e−Ω(m/n)n of the variables take the same value in all satisfying assignments. We also describe a polynomial-time algorithm that finds w.h.p. a satisfying assignment for such formulas.

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18

Severin, �. N. "Sparking as a random process." Journal of Applied Spectroscopy 55, no. 1 (July 1991): 657–61. http://dx.doi.org/10.1007/bf00661715.

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19

Mellein, B., and E. E. Mola. "A multitype random sequential process." Journal of Mathematical Physics 26, no. 3 (March 1985): 514–21. http://dx.doi.org/10.1063/1.526969.

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20

Temnov, G. "Risk Process with Random Income." Journal of Mathematical Sciences 123, no. 1 (September 2004): 3780–94. http://dx.doi.org/10.1023/b:joth.0000036319.21285.22.

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21

Gerke, Stefanie, Dirk Schlatter, Angelika Steger, and Anusch Taraz. "The random planar graph process." Random Structures and Algorithms 32, no. 2 (2008): 236–61. http://dx.doi.org/10.1002/rsa.20186.

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22

Gilboa, Shoni, and Dan Hefetz. "Semi-random process without replacement." Journal of Combinatorics 14, no. 2 (2023): 167–96. http://dx.doi.org/10.4310/joc.2023.v14.n2.a2.

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23

Zhao, Ruiqing, Wansheng Tang, and Cheng Wang. "Fuzzy random renewal process and renewal reward process." Fuzzy Optimization and Decision Making 6, no. 3 (September 1, 2007): 279–95. http://dx.doi.org/10.1007/s10700-007-9012-z.

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24

Zhang Jiangjiang, 张江江, 郭龑强 Guo Yanqiang, 郑治沧 Zheng Zhicang, 林发定 Lin Fading, and 郭晓敏 Guo Xiaomin. "10 Gbit/s量子随机数生成中多路实时高效后处理." Acta Optica Sinica 42, no. 23 (2022): 2327003. http://dx.doi.org/10.3788/aos202242.2327003.

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25

Novikova, Tatyana, Svetlana Evdokimova, and Gotsui Wu. "Development of a quantitative investment algorithm based on Random Forest." Modeling of systems and processes 15, no. 4 (December 13, 2022): 53–60. http://dx.doi.org/10.12737/2219-0767-2022-15-4-53-60.

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In modern research of the stock market, specialists and scientists are improving algorithms and models, combining them with each other, with strategies and market conditions for stock selection. This paper presents an overview of stock selection models for quantitative investment, which was the basis for the proposed procedure and algorithm of quantitative investment, which allow modeling the investment process. The developed algorithm is based on the CART decision tree and Random Forest, which includes the bagging algorithm. The bagging algorithm divides the training set into several new training sets that build their own calculation models, and then their results are summed and integrated to obtain the final prediction. The randomness of Random Forest comes into play in the process of selecting samples from the training dataset and in selecting features to calculate the best split points. However, the proposed strategy is more stable than other stock selection strategies, is more suitable for building quantitative stock selection models, the proposed algorithm has an advantage over other algorithms, and is also more promising for further development.

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26

Wang, Chuanrong. "RANDOM SINGULAR INTEGRAL OF RANDOM PROCESS WITH SECOND ORDER MOMENT." Acta Mathematica Scientia 25, no. 2 (April 2005): 376–84. http://dx.doi.org/10.1016/s0252-9602(17)30295-3.

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27

Park, Kyoung-won, and Sung-bin Cho. "Analysis on Influencing Factors into Smart Watch Users' Satisfaction and Recommendation Intention by Decision Tree Random Forest." Journal of the Korea Management Engineers Society 26, no. 3 (September 30, 2021): 1–21. http://dx.doi.org/10.35373/kmes.26.3.1.

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28

Huang, Jia-Ping, and Ushio Sumita. "EVALUATION OF CUMULATIVE RANDOM SHOCKS GENERATED FROM A SEMI-MARKOV MODULATED POISSON PROCESS AND ITS APPLICATION TO CDO PRICING." Journal of the Operations Research Society of Japan 55, no. 2 (2012): 158–80. http://dx.doi.org/10.15807/jorsj.55.158.

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29

Wagener, J., S. Volgushev, and H. Dette. "The quantile process under random censoring." Mathematical Methods of Statistics 21, no. 2 (April 2012): 127–41. http://dx.doi.org/10.3103/s1066530712020044.

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30

Sun, Zhi Li, Yun Feng Zhang, and Yu Tao Yan. "Experimental Research on Wear Random Process." Advanced Materials Research 126-128 (August 2010): 976–80. http://dx.doi.org/10.4028/www.scientific.net/amr.126-128.976.

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The wear volume is obtained by means of experiment and the wear random process model is established according to the result. The Carbon steel material is used and the samples are grouped five after surface treatment, each group tests six times under the same condition. The wear volume under each wear time shows big dispersion. The additional study indicates that the sample has the large wear volume is in the serious wear state from the beginning, and the wear of running-in phase is inflected by the work velocity and the state condition of the surface of the samples. The wear process which the mean value is a constant and the standard deviation is different is a normal process generally, it is a stationary normal process if the standard deviation has no relation to the start of the wear time, or a Wiener process if the standard deviation is liner with the wear time, it is valuable to forecast the wear reliability.

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31

Nagolkina, Zoya, and Yuri Filonov. "MULTIPLICATIVE APPROXIMATION OF A RANDOM PROCESS." APPLIED GEOMETRY AND ENGINEERING GRAPHICS, no. 100 (May 24, 2021): 205–14. http://dx.doi.org/10.32347/0131-579x.2021.100.205-214.

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In this paper we consider the stochastic Ito differential equation in an infinite-dimensional real Hilbert space. Using the method of multiplicative representations of Daletsky - Trotter, its approximate solution is constructed. Under classical conditions on the coefficients, there is a single to the stochastic equivalence of solutions of the stochastic equation, which is a random process. This development generates an evolutionary family of resolving operators by the formula x(t)= S(t, Construct the division of the segment by the points. An equation with time-uniform coefficients is considered on each elementary segment . There is a single solution of this equation on the elementary segment, which generates the resolving operator by the formula The multiplicative expression is constructed. Using the method of Dalecki-Trotter multiplicative representations, it is proved that this multiplicative expression is stochastically equivalent to the representation generated by the solution of the original equation. This means that the specified multiplicative expression is respectively a representation of the solution of the original equation. That is, the probability of one coincides with the solution of the original stochastic equation. It should be noted that this is possible under additional conditions for the coefficients of the equation. These conditions are the time continuity of the coefficients of the equation. Thus, the constructed multiplicative representation can be interpreted as an approximate solution of the original equation. This method of multiplicative approximation makes it possible to simplify the study of the corresponding random process both at the elementary segment and as a whole. It is known, that the solution of a stochastic equation by a known formula generates a solution of the inverse Kolmogorov equation in the corresponding space. This scheme of multiplicative approximation can be transferred to the solution of the parabolic equation, which is the inverse Kolmogorov equation. Thus, the method of multiplicative approximation makes it possible to simplify the study of both stochastic equations and partial differential equations.

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32

Syuzev, V. V., E. V. Smirnova, and A. V. Proletarsky. "Algorithms of multidimensional random process simulation." Computer Optics 45, no. 4 (July 2021): 627–37. http://dx.doi.org/10.18287/2412-6179-co-770.

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The article discusses two approaches to modeling signals and processes: the method of filter construction and the trigonometric method. It is shown that the later approach is more promising, since an increase in the signal/process representation dimension mathematically means adding a term to the basis function formula, which gives access to fast simulation algorithms. Examples of algorithms for multidimensional simulation of random processes using two methods are given and a software system that implements these algorithms is described. The results provided by the software system will allow you to predict characteristics of engineering projects (accuracy and speed of modeling algorithms). Due to the high relevance of and need for fundamental research of methods and algorithms for digital transformation of the component base, the digitalization of all aspects of activity, including the synthesis of new materials, the development of new methods for designing micro- and nano-systems, the article aims to expand the scope of the spectral method of simulating multidimensional processes using original algorithmic complexes.

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33

Nastić, Aleksandar S., Petra N. Laketa, and Miroslav M. Ristić. "Random environment integer-valued autoregressive process." Journal of Time Series Analysis 37, no. 2 (September 4, 2015): 267–87. http://dx.doi.org/10.1111/jtsa.12161.

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34

Singh, Prashant. "Random acceleration process under stochastic resetting." Journal of Physics A: Mathematical and Theoretical 53, no. 40 (September 17, 2020): 405005. http://dx.doi.org/10.1088/1751-8121/abaf2d.

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35

Bshouty, Daoud, Antonio Di Crescenzo, Barbara Martinucci, and Shelemyahu Zacks. "Generalized Telegraph Process with Random Delays." Journal of Applied Probability 49, no. 3 (September 2012): 850–65. http://dx.doi.org/10.1239/jap/1346955338.

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In this paper we study the distribution of the location, at time t, of a particle moving U time units upwards, V time units downwards, and W time units of no movement (idle). These are repeated cyclically, according to independent alternating renewals. The distributions of U, V, and W are absolutely continuous. The velocities are v = +1 upwards, v = -1 downwards, and v = 0 during idle periods. Let Y+(t), Y−(t), and Y0(t) denote the total time in (0, t) of movements upwards, downwards, and no movements, respectively. The exact distribution of Y+(t) is derived. We also obtain the probability law of X(t) = Y+(t) - Y−(t), which describes the particle's location at time t. Explicit formulae are derived for the cases of exponential distributions with equal rates, with different rates, and with linear rates (leading to damped processes).

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36

Di Crescenzo, Antonio, Antonella Iuliano, Barbara Martinucci, and Shelemyahu Zacks. "Generalized Telegraph Process with Random Jumps." Journal of Applied Probability 50, no. 2 (June 2013): 450–63. http://dx.doi.org/10.1239/jap/1371648953.

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We consider a generalized telegraph process which follows an alternating renewal process and is subject to random jumps. More specifically, consider a particle at the origin of the real line at time t=0. Then it goes along two alternating velocities with opposite directions, and performs a random jump toward the alternating direction at each velocity reversal. We develop the distribution of the location of the particle at an arbitrary fixed time t, and study this distribution under the assumption of exponentially distributed alternating random times. The cases of jumps having exponential distributions with constant rates and with linearly increasing rates are treated in detail.

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37

RAWAL, S., and G. J. RODGERS. "MODELLING INFLATION AS A RANDOM PROCESS." International Journal of Theoretical and Applied Finance 06, no. 08 (December 2003): 821–27. http://dx.doi.org/10.1142/s0219024903002225.

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We introduce a model of inflation in which the prices of commodities are inflated by a random process. At each time step a price x is selected with a rate of xα and is inflated by a factor of 1/β where 0<β<1. For α=0, we obtain a general time-dependent solution, where the initial price distribution can be of any form. When α>0, in the long time limit, only the highest price inflates. For α<0, the model exhibits asymptotic scaling behavior. We also consider the effects of a time dependent β, where 0<β(t)<1, for the case α=0. We find that the price distribution approaches a steady state if β(t)-1~0 faster than 1/t.

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38

Bshouty, Daoud, Antonio Di Crescenzo, Barbara Martinucci, and Shelemyahu Zacks. "Generalized Telegraph Process with Random Delays." Journal of Applied Probability 49, no. 03 (September 2012): 850–65. http://dx.doi.org/10.1017/s002190020000958x.

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In this paper we study the distribution of the location, at time t, of a particle moving U time units upwards, V time units downwards, and W time units of no movement (idle). These are repeated cyclically, according to independent alternating renewals. The distributions of U, V, and W are absolutely continuous. The velocities are v = +1 upwards, v = -1 downwards, and v = 0 during idle periods. Let Y +(t), Y −(t), and Y 0(t) denote the total time in (0, t) of movements upwards, downwards, and no movements, respectively. The exact distribution of Y +(t) is derived. We also obtain the probability law of X(t) = Y +(t) - Y −(t), which describes the particle's location at time t. Explicit formulae are derived for the cases of exponential distributions with equal rates, with different rates, and with linear rates (leading to damped processes).

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39

Di Crescenzo, Antonio, Antonella Iuliano, Barbara Martinucci, and Shelemyahu Zacks. "Generalized Telegraph Process with Random Jumps." Journal of Applied Probability 50, no. 02 (June 2013): 450–63. http://dx.doi.org/10.1017/s0021900200013486.

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We consider a generalized telegraph process which follows an alternating renewal process and is subject to random jumps. More specifically, consider a particle at the origin of the real line at timet=0. Then it goes along two alternating velocities with opposite directions, and performs a random jump toward the alternating direction at each velocity reversal. We develop the distribution of the location of the particle at an arbitrary fixed timet, and study this distribution under the assumption of exponentially distributed alternating random times. The cases of jumps having exponential distributions with constant rates and with linearly increasing rates are treated in detail.

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40

Lichtendahl, Kenneth C. "Random quantiles of the Dirichlet process." Statistics & Probability Letters 79, no. 4 (February 2009): 501–7. http://dx.doi.org/10.1016/j.spl.2008.09.018.

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41

Shah, Sudhir A. "How risky is a random process?" Journal of Mathematical Economics 72 (October 2017): 70–81. http://dx.doi.org/10.1016/j.jmateco.2017.06.005.

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42

Canessa, E., and A. Calmetta. "Physics of a random biological process." Physical Review E 50, no. 1 (July 1, 1994): R47—R49. http://dx.doi.org/10.1103/physreve.50.r47.

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43

Szilágyi, László. "Random Process Simulation Using Petri Nets." MACRo 2015 1, no. 1 (March 1, 2015): 177–82. http://dx.doi.org/10.1515/macro-2015-0017.

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AbstractThis paper introduces a Petri net designed for the simulation of the ancient game called Rock-Paper-Scissors or Roshambo. The network enables us to simulate the behavior of machine players and allows us to design and evaluate strategies against weighted random machine opponents. The paper also presents a theoretical calculus on winning chances. Simulations carried out using the software package Pipe (version 4.3.0) fully confirm the theoretical considerations.

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44

Hansen, Jennie C., and Jerzy Jaworski. "A cutting process for random mappings." Random Structures and Algorithms 30, no. 1-2 (2006): 287–306. http://dx.doi.org/10.1002/rsa.20159.

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45

Wang, Lei, Mingqing Yang, Shiyu Zhang, Chunhui Niu, and Yong Lv. "Perovskite Random Lasers, Process and Prospects." Micromachines 13, no. 12 (November 22, 2022): 2040. http://dx.doi.org/10.3390/mi13122040.

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Random lasers (RLs) are a kind of coherent light source with optical feedback based on disorder-induced multiple scattering effects instead of a specific cavity. The unique feedback mechanism makes RLs different from conventional lasers. They have the advantages of small volume, flexible shape, omnidirectional emission, etc., and have broad application prospects in the fields of laser illumination, speckle-free imaging, display, and sensing. Colloidal metal-halide perovskite nanomaterials are a hot research field in light sources. They have been considered as desired gain media owing to their superior properties, such as high photoluminescence, tunable emission wavelengths, and easy fabrication processes. In this review, we summarize the research progress of RLs based on perovskite nanomaterials. We first present the evolution of the RLs based on the perovskite quantum dots (QDs) and perovskite films. The fabrication process of perovskite nano-/microstructures and lasers is discussed in detail. After that, the frontier applications of perovskite RLs are discussed. Finally, the challenges are discussed, and the prospects for further development are proposed.

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46

Langley, Robin S. "Random matrix theory, random point process theory, and natural frequency statistics." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2442. http://dx.doi.org/10.1121/1.4744649.

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47

Wendler, Martin. "The sequential empirical process of a random walk in random scenery." Stochastic Processes and their Applications 126, no. 9 (September 2016): 2787–99. http://dx.doi.org/10.1016/j.spa.2016.03.002.

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48

Rassoul-Agha, Firas, and Timo Seppäläinen. "Process-level quenched large deviations for random walk in random environment." Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 47, no. 1 (February 2011): 214–42. http://dx.doi.org/10.1214/10-aihp369.

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49

Franke, Brice, and Tatsuhiko Saigo. "A self-similar process arising from a random walk with random environment in random scenery." Bernoulli 16, no. 3 (August 2010): 825–57. http://dx.doi.org/10.3150/09-bej234.

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50

Rataj, Jan, Ivan Saxl, and Karol Pelikán. "Convergence of randomly oscillating point patterns to the Poisson point process." Applications of Mathematics 38, no. 3 (1993): 221–35. http://dx.doi.org/10.21136/am.1993.104548.

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