Tootfinder

Ergodicity and regularity properties of ODEs with semi-Markov switching
Tobias Hurth, Edouard Strickler
https://arxiv.org/abs/2509.25936 https://arxiv.org/…

Ergodicity and regularity properties of ODEs with semi-Markov switching
This paper is devoted to the study of a stochastic process obtained by random switching between a finite collection of vector fields. Such processes have recently been the focus of much attention in the case where the switching times are exponentially distributed, i.e., Markovian switching. In this contribution, we admit any distribution on $\mathbb{R}_+$ as a law for the switching times. We show that whenever this law is not singular with respect to the Lebesgue measure, the stochastic process…

Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Grace Ra Kim, Hailey Warner, Duncan Eddy, Evan Astle, Zachary Booth, Edward Balaban, Mykel J. Kochenderfer
https://arxiv.org/abs/2510.08812

Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning
Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments. We integrate a Bayesian network into the POMDP observation space to manage the high-dimensional and uncertain measurements typical of astrobiology missions…

Adaptive Pseudo-Marginal Algorithm
Sarra Abaoubida, Myl\`ene B\'edard, Florian Maire
https://arxiv.org/abs/2509.24820 https://arxiv.org/pdf/2509.24820

Adaptive Pseudo-Marginal Algorithm
The Pseudo-Marginal (PM) algorithm is a popular Markov chain Monte Carlo (MCMC) method used to sample from a target distribution when its density is inaccessible, but can be estimated with a non-negative unbiased estimator. Its performance depends on a key parameter, N, the number of iterations (or particles) used to approximate the target density. Larger values of N yield more accurate estimates but at increased running time. Previous studies has provided guidelines for selecting an optimal va…