Results 91 to 100 of about 53,922 (203)
Fractional backward stochastic variational inequalities with non-Lipschitz coefficient
Brazilian Journal of Probability and Statistics, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Reinforcement Learning for Jump‐Diffusions, With Financial Applications
Mathematical Finance, EarlyView.ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
wiley +1 more source
Stochastic invariance of closed sets with non-Lipschitz coefficients
, 2016 This paper provides a new characterization of the stochastic invariance of a closed subset of R^d with respect to a diffusion. We extend the well-known inward pointing Stratonovich drift condition to the case where the diffusion matrix can fail to be differentiable: we only assume that the covariance matrix is. In particular, our result can be directly
Jaber, Eduardo Abi +3 more
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Mathematical Finance, EarlyView.ABSTRACT This study develops a novel multivariate stochastic framework for assessing systemic risks, such as climate and nature‐related shocks, within production or financial networks. By embedding a linear stochastic fluid network, interpretable as a generalized vector Ornstein–Uhlenbeck process, into the production network of interdependent ...
Giovanni Amici +3 more
wiley +1 more source
Sensitivity analysis for generalized estimating equation with non‐ignorable missing data
Scandinavian Journal of Statistics, EarlyView.Abstract Many incomplete‐data statistical inference procedures are developed under the missing at random (MAR) assumption. However, the MAR assumption has been criticized as being overly strong for real‐data problems, and is unverifiable by using observed data. To handle data that are missing not at random (MNAR), sensitivity analysis has been proposed
Hui Gong, Kin Wai Chan
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Lifts of continuous and Hölder alpha curves in the configuration space MN/SN$M^N/S_N$
Transactions of the London Mathematical Society, Volume 13, Issue 1, December 2026.Abstract In this paper, we study the quotient space X=MN/SN$X = M^N / S_N$ of equivalence classes of N$N$‐tuples in a metric space (M,dM)$(M, d_M)$, equipped with the metric induced by the minimal total pairing distance. Given a continuous path F:(0,1)→X$F: (0,1) \rightarrow X$, we prove that there exist continuous functions f1,⋯,fN:(0,1)→M$f_1, \dots,
Charles L. Fefferman +3 more
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Comparison theorems for anticipated BSDEs with non-Lipschitz coefficients
Journal of Mathematical Analysis and Applications, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Fourier Mass Lower Bounds for Batchelor‐Regime Passive Scalars
Communications on Pure and Applied Mathematics, Volume 79, Issue 6, Page 1449-1466, June 2026.ABSTRACT Batchelor predicted that a passive scalar ψν$\psi ^\nu$ with diffusivity ν$\nu$, advected by a smooth fluid velocity, should typically have Fourier mass distributed as |ψ̂ν|2(k)≈|k|−d$|\widehat{\psi }^\nu |^2(k) \approx |k|^{-d}$ for |k|≪ν−1/2$|k| \ll \nu ^{-1/2}$.
William Cooperman, Keefer Rowan
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On MAP Estimates and Source Conditions for Drift Identification in SDEs
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT We consider the inverse problem of identifying the drift in an stochastic differential equation (SDE) from n$n$ observations of its solution at M+1$M+1$ distinct time points. We derive a corresponding maximum a posteriori (MAP) estimate, we prove differentiability properties as well as a so‐called tangential cone condition for the forward ...
Daniel Tenbrinck +3 more
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Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT In this work, we present an anisotropic multi‐goal error control based on the dual weighted residual (DWR) method for time‐dependent convection–diffusion–reaction (CDR) equations. Motivated by former work, we combine multiple goals to single error functionals with weights chosen as algorithmic parameters.
Markus Bause +5 more
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