Conditional simulation is a fundamental task in statistical modeling: Generate samples from
the conditionals given finitely many data points from a joint distribution. One promising …

The adapted Wasserstein distance is a metric for quantifying distributional uncertainty and
assessing the sensitivity of stochastic optimization problems on time series data. A …

Biological and artificial neural systems form high-dimensional neural representations that
underpin their computational capabilities. Methods for quantifying geometric similarity in …

We develop comparison results for Markov tree distributions extending ordering results from
the literature on discrete time Markov processes and recently studied ordering results for …

We propose a class of trainable deep learning-based geometries called Neural Spacetimes
(NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) …

Researchers from different areas have independently defined extensions of the usual weak
convergence of laws of stochastic processes with the goal of adequately accounting for the …

Given two probability measures on sequential data, we investigate the transport problem
with time-inconsistent preferences in a discrete-time setting. Motivating examples are …

This dissertation addresses the analysis and modeling of risk and uncertainty, with reference
to both financial and actuarial mathematics. Its goal is to offer valuable insights and establish …

Here **=(** (t)) t≥ 0 is the i-th player's state process driven by the noise process Wi=(Wi (t))
t∈≥ 0 and subject to all players' strategies (v1,···, vN). Each player has the individual …