Relevant bibliographies by topics / Stochastic calculus via regularization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Stochastic calculus via regularization'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Stochastic calculus via regularization"

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Platen, Eckhard, and Rolando Rebolledo. "Pricing via anticipative stochastic calculus." Advances in Applied Probability 26, no. 4 (1994): 1006–21. http://dx.doi.org/10.2307/1427902.

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The paper proposes a general model for pricing of derivative securities. The underlying dynamics follows stochastic equations involving anticipative stochastic integrals. These equations are solved explicitly and structural properties of solutions are studied.
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Platen, Eckhard, and Rolando Rebolledo. "Pricing via anticipative stochastic calculus." Advances in Applied Probability 26, no. 04 (1994): 1006–21. http://dx.doi.org/10.1017/s0001867800026732.

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The paper proposes a general model for pricing of derivative securities. The underlying dynamics follows stochastic equations involving anticipative stochastic integrals. These equations are solved explicitly and structural properties of solutions are studied.
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Atsuji, A. "Nevanlinna Theory via Stochastic Calculus." Journal of Functional Analysis 132, no. 2 (1995): 473–510. http://dx.doi.org/10.1006/jfan.1995.1112.

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Cohen, Paula, Robin Hudson, K. Parthasarathy, and Sylvia Pulmannová. "Hall's transformation via quantum stochastic calculus." Banach Center Publications 43, no. 1 (1998): 147–55. http://dx.doi.org/10.4064/-43-1-147-155.

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Cosso, Andrea, and Francesco Russo. "Functional Itô versus Banach space stochastic calculus and strict solutions of semilinear path-dependent equations." Infinite Dimensional Analysis, Quantum Probability and Related Topics 19, no. 04 (2016): 1650024. http://dx.doi.org/10.1142/s0219025716500247.

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Functional Itô calculus was introduced in order to expand a functional [Formula: see text] depending on time [Formula: see text], past and present values of the process [Formula: see text]. Another possibility to expand [Formula: see text] consists in considering the path [Formula: see text] as an element of the Banach space of continuous functions on [Formula: see text] and to use Banach space stochastic calculus. The aim of this paper is threefold. (1) To reformulate functional Itô calculus, separating time and past, making use of the regularization procedures which match more naturally the
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Barchielli, A., and A. S. Holevo. "Constructing quantum measurement processes via classical stochastic calculus." Stochastic Processes and their Applications 58, no. 2 (1995): 293–317. http://dx.doi.org/10.1016/0304-4149(95)00011-u.

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Luo, Chao, Li Yu, and Jun Zheng. "Extending Stochastic Network Calculus to Loss Analysis." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/918565.

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Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve vi
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OLIVERA, CHRISTIAN. "STOCHASTIC INTEGRATION WITH RESPECT TO THE CYLINDRICAL WIENER PROCESS VIA REGULARIZATION." Infinite Dimensional Analysis, Quantum Probability and Related Topics 16, no. 03 (2013): 1350024. http://dx.doi.org/10.1142/s0219025713500240.

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Following the ideas of F. Russo and P. Vallois, we use the notion of forward integral to introduce a new stochastic integral respect to the cylindrical Wiener process. This integral is an extension of the classical integral. As an application, we prove existence of solution of a parabolic stochastic differential partial equation with anticipating stochastic initial date.
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HUANG, JUAN, HONG CHEN, and LUOQING LI. "LEAST SQUARE REGRESSION WITH COEFFICIENT REGULARIZATION BY GRADIENT DESCENT." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 01 (2012): 1250005. http://dx.doi.org/10.1142/s021969131100447x.

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We propose a stochastic gradient descent algorithm for the least square regression with coefficient regularization. An explicit expression of the solution via sampling operator and empirical integral operator is derived. Learning rates are given in terms of the suitable choices of the step sizes and regularization parameters.
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Meyer-Brandis, Thilo, Bernt Øksendal, and Xun Yu Zhou. "A mean-field stochastic maximum principle via Malliavin calculus." Stochastics 84, no. 5-6 (2012): 643–66. http://dx.doi.org/10.1080/17442508.2011.651619.

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Dissertations / Theses on the topic "Stochastic calculus via regularization"

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Di, Girolami Cristina. "Infinite dimensional stochastic calculus via regularization with financial perspectives." Paris 13, 2010. http://www.theses.fr/2010PA132007.

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Ce document de thèse développe certains aspects du calcul stochastique via régularisation pour des processus X à valeurs dans un espace de Banach général B. Il introduit un concept original de -variation quadratique, où  est un sous-espace du dual d'un produit tensioriel B  B, muni de la topologie projective. Une attention particulière est dévouée au cas où B est l'espace des fonctions continues sur [-, 0],  > 0. Une classe de résultats de stabilité de classe C1 pour des processus ayant une -variation quadratique est établie ainsi que des formules d'Itô pour de tels processus. Un rôle
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DI, GIROLAMI CRISTINA. "Infinite dimensional stochastic calculus via regularization with financial motivations." Doctoral thesis, Luiss Guido Carli, 2010. http://hdl.handle.net/11385/200841.

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Calculus via regularization. [chi]-quadratic variation. Evaluations of [chi]-quadratic variations. Stability of [chi]-quadratic variation and of [chi]-covariation. Ito's formula. A generalized Clark-Ocone formula.
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Teixeira, Nicácio De Messias Alan. "Stochastic Analysis of non-Markovian irregular phenomena." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAE006.

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Cette thèse se concentre sur certains aspects d'analyse stochastique de modèles non-markoviens irréguliers. On formule existence et unicité pour certains problèmes de martingales impliquant deux types de dérive irrégulière perturbée par des fonctionnelles dépendant de la trajectoire. Dans le premier cas, on considère le cas où la dérive est la dérivée d'une fonction continue: le modèle correspondant est celui de milieux aléatoires irréguliers dépendant de la trajectoire. Le second concerne le cas où la dérive est celui d'un processus de Bessel en basse dimension: dans ce cas il est bien connu
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Ashu, Tom A. Ashu. "Non-Smooth SDEs and Hyperbolic Lattice SPDEs Expansions via the Quadratic Covariation Differentiation Theory and Applications." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1500334062680747.

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Books on the topic "Stochastic calculus via regularization"

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Russo, Francesco, and Pierre Vallois. Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0.

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Stochastic Calculus Via Regularizations. Springer International Publishing AG, 2023.

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Vallois, Pierre, and Francesco Russo. Stochastic Calculus Via Regularizations. Springer International Publishing AG, 2022.

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Guionnet, Alice. Free probability. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0003.

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Free probability was introduced by D. Voiculescu as a theory of noncommutative random variables (similar to integration theory) equipped with a notion of freeness very similar to independence. In fact, it is possible in this framework to define the natural ‘free’ counterpart of the central limit theorem, Gaussian distribution, Brownian motion, stochastic differential calculus, entropy, etc. It also appears as the natural setup for studying large random matrices as their size goes to infinity and hence is central in the study of random matrices as their size go to infinity. In this chapter the
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Book chapters on the topic "Stochastic calculus via regularization"

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Russo, Francesco, and Pierre Vallois. "Stochastic Integration via Regularization." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_4.

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Russo, Francesco, and Pierre Vallois. "Calculus via Regularization and Rough Paths." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_17.

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Russo, Francesco, and Pierre Vallois. "Elements of Wiener Analysis." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_10.

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Russo, Francesco, and Pierre Vallois. "Stochastic Calculus with n-Covariations." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_16.

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Russo, Francesco, and Pierre Vallois. "Stability of the Covariation and Itô’s Formula." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_6.

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Russo, Francesco, and Pierre Vallois. "Itô Integrals." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_5.

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Russo, Francesco, and Pierre Vallois. "Weak Dirichlet Processes." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_15.

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Russo, Francesco, and Pierre Vallois. "Itô SDEs with Non-Lipschitz Coefficients." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_13.

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Russo, Francesco, and Pierre Vallois. "Hermite Polynomials and Wiener Chaos Expansion." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_9.

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Russo, Francesco, and Pierre Vallois. "Change of Probability and Martingale Representation." In Stochastic Calculus via Regularizations. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09446-0_7.

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Conference papers on the topic "Stochastic calculus via regularization"

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Hajas, Tamás, Gabor Tolnai, Elod Pazman, and David Legrady. "Optimization of the Forced Fission Coefficient via Stochastic Calculus." In International Conference on Physics of Reactors (PHYSOR 2024). American Nuclear Society, 2024. http://dx.doi.org/10.13182/physor24-43571.

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Adamuz-Hinojosa, Oscar, Lanfranco Zanzi, Vincenzo Sciancalepore, Andres Garcia-Saavedra, and Xavier Costa-Pérez. "ORANUS: Latency-tailored Orchestration via Stochastic Network Calculus in 6G O-RAN." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications. IEEE, 2024. http://dx.doi.org/10.1109/infocom52122.2024.10621356.

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Zheng, Jun, Li Yu, and Peng Yang. "Throughput analysis of cognitive radio networks via stochastic network calculus." In 2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2014. http://dx.doi.org/10.1109/wcsp.2014.6992170.

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Lecca, P., C. Priami, C. Laudanna та G. Constantin. "Predicting cell adhesion probability via the biochemical stochastic π-calculus". У the 2004 ACM symposium. ACM Press, 2004. http://dx.doi.org/10.1145/967900.967944.

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Guan, Yue, Qifan Zhang, and Panagiotis Tsiotras. "Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/339.

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We explore the use of policy approximations to reduce the computational cost of learning Nash equilibria in zero-sum stochastic games. We propose a new Q-learning type algorithm that uses a sequence of entropy-regularized soft policies to approximate the Nash policy during the Q-function updates. We prove that under certain conditions, by updating the entropy regularization, the algorithm converges to a Nash equilibrium. We also demonstrate the proposed algorithm's ability to transfer previous training experiences, enabling the agents to adapt quickly to new environments. We provide a dynamic
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Priezzhev, Ivan, Dmitry Danko, and Uwe Strecker. "New-Age Kolmogorov Full-Function Neural Network KNN Offers High-Fidelity Reservoir Predictions via Estimation of Core, Well Log, Map and Seismic Properties." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207575-ms.

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Abstract Instead of relying on analytical functions to approximate property relationships, this innovative hybrid neural network technique offers highly adaptive, full-function (!) predictions that can be applied to different subsurface data types ranging from (1.) core-to-log prediction (permeability), (2.) multivariate property maps (oil-saturated thickness maps), and, (3.) petrophysical properties from 3D seismic data (i.e., hydrocarbon pore volume, instantaneous velocity). For each scenario a separate example is shown. In case study 1, core measurements are used as the target array and wel
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