Littérature scientifique sur le sujet « Monotone bounded integration »
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Articles de revues sur le sujet "Monotone bounded integration"
Cerreia-Vioglio, S., F. Maccheroni, M. Marinacci et L. Montrucchio. « Commutativity, comonotonicity, and Choquet integration of self-adjoint operators ». Reviews in Mathematical Physics 30, no 10 (12 octobre 2018) : 1850016. http://dx.doi.org/10.1142/s0129055x18500162.
Texte intégralMessina, Eleonora, Mario Pezzella et Antonia Vecchio. « Positive Numerical Approximation of Integro-Differential Epidemic Model ». Axioms 11, no 2 (9 février 2022) : 69. http://dx.doi.org/10.3390/axioms11020069.
Texte intégralBian, Chao, Chao Feng, Chao Qian et Yang Yu. « An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 04 (3 avril 2020) : 3267–74. http://dx.doi.org/10.1609/aaai.v34i04.5726.
Texte intégralN Nyaga, Victoria, Marc Arbyn et Marc Aerts. « Beta-binomial analysis of variance model for network meta-analysis of diagnostic test accuracy data ». Statistical Methods in Medical Research 27, no 8 (14 décembre 2016) : 2554–66. http://dx.doi.org/10.1177/0962280216682532.
Texte intégralMAJUMDER, SUBHASHIS, SUBHAS C. NANDY et BHARGAB B. BHATTACHARYA. « ON FINDING A STAIRCASE CHANNEL WITH MINIMUM CROSSING NETS IN A VLSI FLOORPLAN ». Journal of Circuits, Systems and Computers 13, no 05 (octobre 2004) : 1019–38. http://dx.doi.org/10.1142/s0218126604001854.
Texte intégralRuby et Moumita Mandal. « The geometrical and physical interpretation of fractional order derivatives for a general class of functions ». Mathematical Methods in the Applied Sciences, 14 mars 2024. http://dx.doi.org/10.1002/mma.10020.
Texte intégralThèses sur le sujet "Monotone bounded integration"
Basak, Subhasish. « Multipathogen quantitative risk assessment in raw milk soft cheese : monotone integration and Bayesian optimization ». Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG021.
Texte intégralThis manuscript focuses on Bayesian optimization of a quantitative microbiological risk assessment (QMRA) model, in the context of the European project ArtiSaneFood, supported by the PRIMA program. The primary goal is to establish efficient bio-intervention strategies for cheese producers in France.This work is divided into three broad directions: 1) development and implementation of a multipathogen QMRA model for raw milk soft cheese, 2) studying monotone integration methods for estimating outputs of the QMRA model, and 3) designing a Bayesian optimization algorithm tailored for a stochastic and computationally expensive simulator.In the first part we propose a multipathogen QMRA model, built upon existing studies in the literature (see, e.g., Bonifait et al., 2021, Perrin et al., 2014, Sanaa et al., 2004, Strickland et al., 2023). This model estimates the impact of foodborne illnesses on public health, caused by pathogenic STEC, Salmonella and Listeria monocytogenes, which can potentially be present in raw milk soft cheese. This farm-to-fork model also implements the intervention strategies related to mlik and cheese testing, which allows to estimate the cost of intervention. An implementation of the QMRA model for STEC is provided in R and in the FSKX framework (Basak et al., under review). The second part of this manuscript investigates the potential application of sequential integration methods, leveraging the monotonicity and boundedness properties of the simulator outputs. We conduct a comprehensive literature review on existing integration methods (see, e.g., Kiefer, 1957, Novak, 1992), and delve into the theoretical findings regarding their convergence. Our contribution includes proposing enhancements to these methods and discussion on the challenges associated with their application in the QMRA domain.In the final part of this manuscript, we propose a Bayesian multiobjective optimization algorithm for estimating the Pareto optimal inputs of a stochastic and computationally expensive simulator. The proposed approach is motivated by the principle of Stepwise Uncertainty Reduction (SUR) (see, e.g., Vazquezand Bect, 2009, Vazquez and Martinez, 2006, Villemonteix et al., 2007), with a weighted integrated mean squared error (w-IMSE) based sampling criterion, focused on the estimation of the Pareto front. A numerical benchmark is presented, comparing the proposed algorithm with PALS (Pareto Active Learning for Stochastic simulators) (Barracosa et al., 2021), over a set of bi-objective test problems. We also propose an extension (Basak et al., 2022a) of the PALS algorithm, tailored to the QMRA application case
Rapports d'organisations sur le sujet "Monotone bounded integration"
Bonatti, Piero, Carsten Lutz et Frank Wolter. Expressive Non-Monotonic Description Logics Based on Circumscription. Technische Universität Dresden, 2005. http://dx.doi.org/10.25368/2022.149.
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