All Research Papers Published In Stochastic Processes and their Applications

Research Article
10.1016/j.spa.2025.104833

Comparison theorems for mean-field BSDEs whose generators depend on the law of the solution <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si8.svg" display="inline" id="d1e73"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>Y</mml:mi> <mml:mo>,</mml:mo> <mml:mi>Z</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math>

Mar 01, 2026
Stochastic Processes and their Applications
Juan Li + 2 more +2

Research Article
10.1016/j.spa.2025.104835

Convergence rate for the coupon collector’s problem with Stein’s method

Mar 01, 2026
Stochastic Processes and their Applications
Bruno Costacèque + 1 more +1

Research Article
10.1016/j.spa.2026.104921

On the maximal displacement of some critical branching Lévy processes with stable offspring distribution

Feb 18, 2026
Stochastic Processes and their Applications
Christophe Profeta

Research Article
1
10.1016/j.spa.2025.104832

Multivariate change estimation for a stochastic heat equation from local measurements

Feb 01, 2026
Stochastic Processes and their Applications
Anton Tiepner + 1 more +1

Research Article
1
10.1016/j.spa.2025.104793

Scaling limit for small blocks in the Chinese restaurant process

Feb 01, 2026
Stochastic Processes and their Applications
Oleksii Galganov + 1 more +1

The Chinese restaurant process is a basic sequential construction of consistent random partitions. We consider random point measures describing the composition of small blocks in such partitions and show that their scaling limit is given by the projective limit of certain inhomogeneous Poisson measures on cones of increasing dimension. This result makes it possible to derive classical and functional limit theorems in the Skorokhod topology for various characteristics of the Chinese restaurant process.

Research Article
10.1016/j.spa.2025.104806

Guided smoothing and control for diffusion processes

Feb 01, 2026
Stochastic Processes and their Applications
Oskar Eklund + 2 more +2

The smoothing distribution is the conditional distribution of the diffusion process in the space of trajectories given noisy observations made continuously in time. It is generally difficult to sample from this distribution. We use the theory of enlargement of filtrations to show that the conditional process has an additional drift term derived from the backward filtering distribution that is moving or guiding the process towards the observations. This term is intractable, but its effect can be equally introduced by replacing it with a heuristic, where importance weights correct for the discrepancy. From this Markov Chain Monte Carlo and sequential Monte Carlo algorithms are derived to sample from the smoothing distribution. The choice of the guiding heuristic is discussed from an optimal control perspective and evaluated. The results are tested numerically on a stochastic differential equation for reaction–diffusion.

Research Article
10.1016/j.spa.2025.104867

Asymptotic behaviors of subcritical branching killed Lévy processes

Jan 01, 2026
Stochastic Processes and their Applications
Yan-Xia Ren + 2 more +2

Research Article
10.1016/j.spa.2025.104781

Holomorphic jump-diffusions

Jan 01, 2026
Stochastic Processes and their Applications
Christa Cuchiero + 2 more +2

Research Article
10.1016/j.spa.2025.104764

Parallelized midpoint randomization for Langevin Monte Carlo

Dec 01, 2025
Stochastic Processes and their Applications
Lu Yu + 1 more +1

Research Article
1
10.1016/j.spa.2025.104758

Weighted solutions of random time horizon BSDEs with stochastic monotonicity and general growth generators and related PDEs

Dec 01, 2025
Stochastic Processes and their Applications
Xinying Li + 2 more +2