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- Fluctuations of additive martingale limits of branching Brownian motion
- A system-centered probabilistic formalism linking multiple equilibria and biodiversity in ecological and evolutionary models
- Freidlin-Wentzell type exit-time estimates for time-inhomogeneous diffusions and their applications
- Gumbel convergence for maximal packing distances from uniform random samples
- Second-order fields for stochastic partial differential equations
- Linear Preservers of Infinitely Divisible Matrices
- Stability of Fork-Join Systems with Redundancy and Heterogeneous Servers
- Complete Monotonicity of the Srivastava-Tomovski Function and Its Laplace-Wright Realization: Necessary and Sufficient Conditions
- Tensor Network Moral Graph Recovery of Discrete Probability Distributions
- Quantitative Parisi formulas and fluctuations in the Sherrington-Kirkpatrick model
- Weak synchronisation for McKean--Vlasov SDEs
- Pathwise Global-in-Time Existence for the generalised KPZ Equation in the Full Subcritical Regime
- Quadratic Point Estimate Method for Uncertainty Quantification with Dependent Non-Gaussian Inputs
- Endpoint and Vanishing-Density Asymptotics for Hardy--Szegő Zero Counts
- Polynomial mixing for the weakly damped stochastic nonlinear Schrödinger equation on the whole space
- Optimal Covariance Inflation under Gaussian Tilts
- On the Stability of the Independence Number in Random Distance Graphs
- High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
- Strong convergence rates of tamed exponential Euler schemes for superlinear hyperbolic SPDEs
- Low-Temperature Large Deviations for the Two-Dimensional Wick-Ordered Cubic Wave Equation
- MA(1) processes with Laplace innovations conditioned to stay positive
- The circular law for non-Hermitian random band matrices: optimal bandwidth, periodic profile and discrete law
- Equi-dependence implying independence
- Sharp Normalized Covariance Bounds and Constant-Stretch Correlated Sampling on the Hypersimplex
- Any-Dimensional Learning by Sampling