We factorize probability mass functions of discrete distributions belonging to Panjer's family and to its certain extensions to define a stochastic order on the space of distributions supported on . Main properties of this order are presented. Comparison of some well-known distributions with respect to this order allows to generate new families of distributions that satisfy various recurrrence relations. The recursion formula for the probabilities of corresponding compound distributions for one such family is derived. Applications to various domains of reliability theory are provided.
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http://dx.doi.org/10.1007/s00184-021-00822-5 | DOI Listing |
Phys Life Rev
December 2024
Community Healthcare Center Dr. Adolf Drolc Maribor, Ulica talcev 9, 2000 Maribor, Slovenia; Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia; Complexity Science Hub, Metternichgasse 8, 1080 Vienna, Austria; Department of Physics, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea. Electronic address:
Synchrony in neuronal networks is crucial for cognitive functions, motor coordination, and various neurological disorders. While traditional research has focused on pairwise interactions between neurons, recent studies highlight the importance of higher-order interactions involving multiple neurons. Both types of interactions lead to complex synchronous spatiotemporal patterns, including the fascinating phenomenon of chimera states, where synchronized and desynchronized neuronal activity coexist.
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January 2025
CAAC Academy, Civil Aviation Flight University of China, Chengdu, 618307, China.
In practical supply chain operations, efficient order allocation significantly enhances the overall efficiency of the supply chain. Real production environments are plagued by numerous uncertainties, such as unpredictable customer orders, which greatly amplify the complexity of solving practical allocation problems. This study focuses on the problem of allocating orders to parallel machines with varying efficiencies under uncertain and high-dimensional conditions.
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January 2025
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs.
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December 2024
School of Computer and Information Engineering, Hubei Normal University, Huangshi, 435002, China.
For finely representation of complex reservoir units, higher computing overburden and lower spatial resolution are limited to traditional stochastic simulation. Therefore, based on Generative Adversarial Networks (GANs), spatial distribution patterns of regional variables can be reproduced through high-order statistical fitting. However, parameters of GANs cannot be optimized under insufficient training samples.
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December 2024
Department of Physics, University of Trento, Via Sommarive 14, 38123, Povo (TN), Italy.
It has been argued that realistic models of (singularity-free) black holes (BHs) embedded within an expanding Universe are coupled to the large-scale cosmological dynamics, with striking consequences, including pure cosmological growth of BH masses. In this pilot study, we examine the consequences of this growth for the stochastic gravitational wave background (SGWB) produced by inspiraling supermassive cosmologically coupled BHs. We show that the predicted SGWB amplitude is enhanced relative to the standard uncoupled case, while maintaining the [Formula: see text] frequency scaling of the spectral energy density.
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