AI & ML
Geometric Functional Inequalities for Markov Processes: An Invitation via the Langevin Diffusion
arXiv stat.CO0 views
arXiv:2611.07785v1 Announce Type: cross Abstract: Functional inequalities provide a language for reasoning about the long-time properties of ergodic Markov processes in terms of their macroscopic geometry. This article offers an informal introduction to functional inequalities for readers who are more familiar with probabilistic tools based around couplings, Lyapunov techniques, and similar. The main ideas are developed through the overdamped Langevin diffusion, where various functionals related to this convergence admit particularly transparent expressions, and for which the key associated inequalities -- namely, the Poincar\'{e} and logarithmic Sobolev inequalities -- are especially well-studied. We explain the qualitative content of these inequalities, what they imply about convergence to equilibrium, how they transform and compose under natural operations, and how they interface with classical probabilistic approaches. Related inequalities are discussed more briefly, and an annotated guide is provided for readers wishing to enter the wider literature.
Source:https://arxiv.org/abs/2611.07785PreviousA Unified Coordinate Descent Framework for Penalized Longitudinal Log-Contrast Quantile RegressionNextEfficiently Estimating Cycle Statistics on Graphs via Spanning Trees
Related posts
cBridge: Do You Need Gas on Both Chains?
0dev.to
Honor Puts a 4-DOF Titanium Gimbal Inside a Phone at 7,999 Yuan
0dev.to

Protesters break into Microsoft data center site in Amsterdam
0The Verge

After printing 40,000 whistles, this is the 3D printer I’d buy
0The Verge

A New Community For In-House UX People
0Smashing Magazine
A flaw in ChatGPT, a lesson for our front-ends
0dev.to