Results 171 to 180 of about 419,601 (272)

RANDOM EVOLUTIONS, MARKOV CHAINS, AND SYSTEMS OF PARTIAL DIFFERENTIAL EQUATIONS [PDF]

, 1969
Richard J. Griego, R. Hersh
openalex   +1 more source

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao, Qintao Guo, Jin Zhou, Cheng Yi   +3 more
wiley   +1 more source

Approximating the ideal observer for joint signal detection and estimation tasks by the use of Markov-Chain Monte Carlo with generative adversarial networks. [PDF]

J Med Imaging (Bellingham)
Li D, Li K, Zhou W, Anastasio MA.
europepmc   +1 more source

Mine ventilation system reliability evaluation based on a Markov chain. [PDF]

Sci Rep, 2022
Liu L, Liu J, Zhou Q.
europepmc   +1 more source

A Probabilistic Cut Finite Element Method With Random Field Generator and Bayesian Model Calibration for Flow Through Rough Cracks

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT A new model for the simulation of fluid flow through rough cracks is presented. The model combines a probabilistic cut finite element method (PCutFEM) to capture the unfitted boundary condition at the fluid interface, with a stochastic random field generator to represent the crack asperities.
Brubeck Lee Freeman, Evan John Ricketts, Diane Gardner, Anthony Jefferson, Peter John Cleall, Pierre Kerfriden   +5 more
wiley   +1 more source

Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks. [PDF]

Front Artif Intell
Jawla D, Kelleher J.
europepmc   +1 more source

Finite‐Horizon Behavior of the Periodically Observed Time‐Homogeneous Poisson Process

Naval Research Logistics (NRL), EarlyView.
ABSTRACT When evaluating whether system arrivals can be characterized as a Poisson process, researchers draw conclusions from observed data. In practice, many systems are observed periodically (e.g., hourly, daily, monthly), which leads to discretization errors in the observed data.
Zachary T. Hornberger, Douglas M. King, Sheldon H. Jacobson   +2 more
wiley   +1 more source

Assessment of cloud service trusted state based on fuzzy entropy and Markov chain. [PDF]

Sci Rep
Yang M, Jiang R, Wang J, Gui B, Long L.
europepmc   +1 more source

Markov chain analysis indicates that positive and negative emotions have abnormal temporal interactions during daily life in schizophrenia. [PDF]

J Psychiatr Res, 2023
Strauss GP, Zamani Esfahlani F, Raugh IM, Luther L, Sayama H.   +4 more
europepmc   +1 more source

STRONG MIXING PROPERTIES OF MARKOV CHAINS WITH INFINITE INVARIANT MEASURE [PDF]

, 1967
Klaus Krickeberg
openalex   +1 more source