Results 41 to 50 of about 776,491 (337)
Robust Average-Reward Markov Decision Processes [PDF]
AAAI Conference on Artificial Intelligence, 2023 In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs.
Yue Wang +4 more
semanticscholar +1 more source
Should I stay or should I go? A habitat-dependent dispersal kernel improves prediction of movement. [PDF]
PLoS ONE, 2011 The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals.
Fabrice Vinatier +5 more
doaj +1 more source
Evolution and prediction of land use around metro stations
Frontiers in Environmental Science, 2023 Metro stations are considered high-quality resources for promoting urban development, which have great influences on the surrounding land use changes. The simulation and prediction of land use change can provide a scientific basis for urban land planning.
Fei Fu +6 more
doaj +1 more source
Chain of Log-Concave Markov Chains [PDF]
International Conference on Learning Representations, 2023 We introduce a theoretical framework for sampling from unnormalized densities based on a smoothing scheme that uses an isotropic Gaussian kernel with a single fixed noise scale. We prove one can decompose sampling from a density (minimal assumptions made
Saeed Saremi, Ji Won Park, F. Bach
semanticscholar +1 more source
Weighted Nash Inequalities [PDF]
, 2010 Nash or Sobolev inequalities are known to be equivalent to ultracontractive properties of Markov semigroups, hence to uniform bounds on their kernel densities.
Bakry, Dominique +3 more
core +6 more sources
Quantitative Approximation of the Probability Distribution of a Markov Process by Formal Abstractions [PDF]
Logical Methods in Computer Science, 2015 The goal of this work is to formally abstract a Markov process evolving in discrete time over a general state space as a finite-state Markov chain, with the objective of precisely approximating its state probability distribution in time, which allows for
Sadegh Esmaeil Zadeh Soudjani +1 more
doaj +1 more source
Sufficient stochastic maximum principle for the optimal control of semi-Markov modulated jump-diffusion with application to Financial optimization [PDF]
, 2014 The finite state semi-Markov process is a generalization over the Markov chain in which the sojourn time distribution is any general distribution. In this article we provide a sufficient stochastic maximum principle for the optimal control of a semi ...
Deshpande, Amogh
core +2 more sources
Derivatives of Markov Kernels and Their Jordan Decomposition [PDF]
Journal of Applied Analysis, 2008 Let \((P_\vartheta)_{\vartheta \in \Theta}\) be a parametric family of Markov kernels from a measurable space \((X, \mathcal{X})\) to a locally compact space \(Y\). The family \((P_\vartheta)_{\vartheta \in \Theta}\) is called weakly differentiable at \(\vartheta\) if for any \(x \in X\) there is a finite signed Baire measure \(P'_\vartheta(x, .)\) on \
Heidergott, B.F. +2 more
openaire +3 more sources
Agriculture, 2021 Based on the grain production data of the counties (cities, districts) in Poyang Lake Basin, this paper uses the productivity index of Epsilon Based Measure of Malmquist Luenberger (EBM-ML Index) to analyse the green total factor productivity (GTFP) of ...
Bingfei Bao +4 more
doaj +1 more source
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces [PDF]
Neural Information Processing Systems, 2022 We study a class of dynamical systems modelled as Markov chains that admit an invariant distribution via the corresponding transfer, or Koopman, operator.
V. Kostić +5 more
semanticscholar +1 more source