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Related papers: An Optimal Skorokhod Embedding for Diffusions

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While efficient distribution learning is no doubt behind the groundbreaking success of diffusion modeling, its theoretical guarantees are quite limited. In this paper, we provide the first rigorous analysis on approximation and…

Machine Learning · Statistics 2023-03-06 Kazusato Oko , Shunta Akiyama , Taiji Suzuki

Among all generalized Ornstein-Uhlenbeck processes which sample the same invariant measure and for which the same amount of randomness (a $N$-dimensional Brownian motion) is injected in the system, we prove that the asymptotic rate of…

Probability · Mathematics 2021-10-07 Arnaud Guillin , Pierre Monmarché

Given an initial (resp., terminal) probability measure $\mu$ (resp., $\nu$) on $\mathbb{R}^d$, we characterize those optimal stopping times $\tau$ that maximize or minimize the functional $\mathbb{E} |B_0 - B_\tau|^{\alpha}$, $\alpha > 0$,…

Probability · Mathematics 2017-11-09 Nassif Ghoussoub , Young-Heon Kim , Tongseok Lim

The hierarchical equations of motion theory for Drude dissipation is optimized, with a convenient convergence criterion proposed in advance of numerical propagations. The theoretical construction is on basis of a Pad\'{e} spectrum…

Quantum Physics · Physics 2011-07-04 Jin-Jin Ding , Jian Xu , Jie Hu , Rui-Xue Xu , YiJing Yan

Maximal couplings are (probabilistic) couplings of Markov processes such that the tail probabilities of the coupling time attain the total variation lower bound (Aldous bound) uniformly for all time. Markovian (or immersion) couplings are…

Probability · Mathematics 2016-03-29 Sayan Banerjee , Wilfrid S. Kendall

We study the least-energy way to reshape a probability distribution when motion is constrained to a horizontal bundle, that is, optimal transport and distribution steering in sub-Riemannian geometry, motivated by density control over…

Optimization and Control · Mathematics 2026-05-18 Daniel Owusu Adu , Karthik Elamvazhuthi , Bahman Gharesifard

In this paper, we provide an alternative proof of the monotonicity principle for the optimal Skorokhod embedding problem established by Beiglb\"ock, Cox and Huesmann. This principle presents a geometric characterization that reflects the…

Probability · Mathematics 2016-08-04 Gaoyue Guo , Xiaolu Tan , Nizar Touzi

We consider the mean time to absorption by an absorbing target of a diffusive particle with the addition of a process whereby the particle is reset to its initial position with rate $r$. We consider several generalisations of the model of…

Statistical Mechanics · Physics 2015-11-24 Martin R. Evans , Satya N. Majumdar

We solve the problem of optimal stopping of a Brownian motion subject to the constraint that the stopping time's distribution is a given measure consisting of finitely-many atoms. In particular, we show that this problem can be converted to…

Optimization and Control · Mathematics 2017-07-07 Erhan Bayraktar , Christopher W. Miller

We prove existence and uniqueness for semimartingale reflecting diffusions in 2-dimensional piecewise smooth domains with varying, oblique directions of reflection on each "side", under geometric, easily verifiable conditions. Our…

Probability · Mathematics 2024-07-31 Cristina Costantini , Thomas G. Kurtz

Understanding the spread of infectious diseases requires integrating movement, physical constraints, and spatial configurations into epidemiological models. In this study, we investigate how particle diffusivity, hardcore interactions, and…

Other Condensed Matter · Physics 2025-06-17 Kaito Takahashi , Makiko Sasada , Takuma Akimoto

Brownian diffusion subject to stochastic resetting to a fixed position has been widely studied for applications to random search processes. In an unbounded domain, the mean first-passage time at a target site can be minimized for a…

Statistical Mechanics · Physics 2025-10-08 Pedro Julián-Salgado , Leonardo Dagdug , Denis Boyer

We study the infinite-horizon average (ergodic) risk sensitive control problem for diffusion processes under a general structural hypothesis: there is a partition of state space into two subsets, where the controlled diffusion process…

Optimization and Control · Mathematics 2025-12-01 Sumith Reddy Anugu , Guodong Pang

We solve an optimal stopping problem where the underlying diffusion is Brownian motion on $\bf R$ with a positive drift changing at zero. It is assumed that the drift $\mu_1$ on the negative side is smaller than the drift $\mu_2$ on the…

Probability · Mathematics 2018-11-15 Ernesto Mordecki , Paavo Salminen

We introduce numerical methods for simulating the diffusive motion of rigid bodies of arbitrary shape immersed in a viscous fluid. We parameterize the orientation of the bodies using normalized quaternions, which are numerically robust,…

Soft Condensed Matter · Physics 2015-10-28 Steven Delong , Florencio Balboa Usabiaga , Aleksandar Donev

Sticky Brownian motion is the simplest example of a diffusion process that can spend finite time both in the interior of a domain and on its boundary. It arises in various applications such as in biology, materials science, and finance.…

Numerical Analysis · Mathematics 2020-07-21 Nawaf Bou-Rabee , Miranda Holmes-Cerfon

The conformal Skorokhod embedding problem (CSEP) is a planar variant of the classical problem where the solution is now a simply connected domain $D\subset\mathbb{C}$ whose exit time embeds a given probability distribution $\mu$ by…

Probability · Mathematics 2020-06-03 Phanuel Mariano , Hugo Panzo

The Az\'{e}ma-Yor solution (resp., the Perkins solution) of the Skorokhod embedding problem has the property that it maximizes (resp., minimizes) the law of the maximum of the stopped process. We show that these constructions have a wider…

Probability · Mathematics 2013-09-10 David Hobson , Martin Klimmek

An unbiased shift of the two-sided Brownian motion $(B_t \colon t\in{\mathbb R})$ is a random time $T$ such that $(B_{T+t} \colon t\in{\mathbb R})$ is still a two-sided Brownian motion. Given a pair $\mu, \nu$ of orthogonal probability…

Probability · Mathematics 2017-12-06 Peter Morters , Istvan Redl

Diffusion models have become the go-to method for large-scale generative models in real-world applications. These applications often involve data distributions confined within bounded domains, typically requiring ad-hoc thresholding…

Machine Learning · Statistics 2024-01-09 Wei Deng , Yu Chen , Nicole Tianjiao Yang , Hengrong Du , Qi Feng , Ricky T. Q. Chen