Registering other subjects' PET images into a target subject's MR anatomy creates training data that, when used to pre-train a diffusion model, improves low-count PET reconstruction accuracy.
The convergence rate from discrete to continuous optimal investment stopping problem
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abstract
We study the optimal investment stopping problem in both continuous and discrete case, where the investor needs to choose the optimal trading strategy and optimal stopping time concurrently to maximize the expected utility of terminal wealth. Based on the work [9] with an additional stochastic payoff function, we characterize the value function for the continuous problem via the theory of quadratic reflected backward stochastic differential equation (BSDE for short) with unbounded terminal condition. In regard to discrete problem, we get the discretization form composed of piecewise quadratic BSDEs recursively under Markovian framework and the assumption of bounded obstacle, and provide some useful prior estimates about the solutions with the help of auxiliary forward-backward SDE system and Malliavin calculus. Finally, we obtain the uniform convergence and relevant rate from discretely to continuously quadratic reflected BSDE, which arise from corresponding optimal investment stopping problem through above characterization.
fields
physics.med-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction
Registering other subjects' PET images into a target subject's MR anatomy creates training data that, when used to pre-train a diffusion model, improves low-count PET reconstruction accuracy.