most citedCompensating random transition-detection blackouts in Markov networks

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cond-mat.stat-mech2026

Generalizing the multidimensional thermodynamic uncertainty relation to combinations of arbitrary counting variables

Niklas Buschmann, Udo Seifert, Alexander M. Maier

Uncertainty relations provide lower bounds for otherwise hidden quantities of a partially accessible Markov network like the mean entropy production rate and the total dynamical ac…

cond-mat.stat-mech2026

Lower bounds on entropy production from dynamical correlation functions

Paul Raux, Alexander M. Maier, Udo Seifert

Entropy production is a key property in stochastic thermodynamics. For partially observed and coarse-grained systems, its inference is challenging and typically rests on proven low…

cond-mat.stat-mech20261 cited

Compensating random transition-detection blackouts in Markov networks

Alexander M. Maier, Benjamin Häsler, Udo Seifert

In Markov networks, measurement blackouts with unknown frequency compromise observations such that thermodynamic quantities can no longer be inferred reliably. In particular, the o…

cond-mat.stat-mech2025

A pedestrian's approach to large deviations in semi-Markov processes with an application to entropy production

Alexander M. Maier, Jonas H. Fritz, Udo Seifert

Semi-Markov processes play an important role in the effective description of partially accessible systems in stochastic thermodynamics. They occur, for instance, in coarse-graining…

cond-mat.stat-mech2025

From observed transitions to hidden paths in Markov networks

Alexander M. Maier, Udo Seifert, Jann van der Meer

The number of observable degrees of freedom is typically limited in experiments. Here, we consider discrete Markov networks in which an observer has access to a few visible transit…