Listar por tema "Markov processes"
Mostrando ítems 1-20 de 24
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Analytical solution and optimisation of serial supply chains with multiple nodes, lost sales, continuous review replenishment policies, stochastic lead times and external demand
(2022)This work analyses a serial supply chain with an arbitrary number of nodes (retailer, wholesaler, manufacturer, supplier, etc.). Every node faces supply uncertainty in both replenishment lead time and quantity delivered ... -
Approximate Bayesian computation for granular and molecular dynamics simulations
(2016)The effective integration of models with data through Bayesian uncertainty quantification hinges on the formulation of a suitable likelihood function. In many cases such a likelihood may not be readily available or it may ... -
Bayesian annealed sequential importance sampling: An unbiased version of transitional Markov chain Monte Carlo
(2018)The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel computing architectures. ... -
Bayesian damage characterization based on probabilistic model of scattering coefficients and hybrid wave finite element model scheme
(2019)Ultrasonic Guided Wave(GW) has been proven to be sensitive to small damage. Motivated by the fact that the quantitative relationship between wave scattering and damage intensity can be described by scattering properties, ... -
Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
(2020)Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. ... -
Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
(2012)We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation ... -
Exploiting task-based parallelism in Bayesian Uncertainty Quantification
(2015)We introduce a task-parallel framework for non-intrusive Bayesian Uncertainty Quantification and Propagation of complex and computationally demanding physical models on massively parallel computing architectures. The ... -
Fusing heterogeneous data for the calibration of molecular dynamics force fields using hierarchical Bayesian models
(2016)We propose a hierarchical Bayesian framework to systematically integrate heterogeneous data for the calibration of force fields in Molecular Dynamics (MD) simulations. Our approach enables the fusion of diverse experimental ... -
Fuzzy cognitive maps and multi-step gradient methods for prediction: Applications to electricity consumption and stock exchange returns
(2015)The paper focuses on the application of fuzzy cognitive map (FCM) with multi-step learning algorithms based on gradient method and Markov model of gradient for prediction tasks. Two datasets were selected for the implementation ... -
Generalized Hankel matrix for the minimal realization of singular systems
(1996)A generalized Hankel matrix (in pencil form) is introduced. Based upon this definition the dimension of the minimal realization and the controllability and observability properties are related in a straight forward way. ... -
A hierarchical Bayesian framework for force field selection in molecular dynamics simulations
(2016)We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different ... -
Implementation of an adaptive meta-model for Bayesian finite element model updating in time domain
(2017)This work explores the feasibility of integrating an adaptive meta-model into a finite element model updating formulation using dynamic response data. A Bayesian model updating approach based on a stochastic simulation ... -
Markov decision processes with multidimensional action spaces
(2010)We study controlled Markov processes where multiple decisions need to be made for each state. We present conditions on the cost structure and the state transition mechanism of the process under which optimal decisions are ... -
Meta-reinforcement learning via buffering graph signatures for live video streaming events
(2021)In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated ... -
Modeling the dynamics of caching in content-based publish/subscribe systems
(2011)This paper considers cache dimensioning in the context of publish/subscribe (pub/sub) systems. We assume that each broker is equipped with a limited capacity cache and it decides upon a policy for caching and prioritizing ... -
On the potential of dynamic sub-structuring methods for model updating
(2019)While purely data-driven assessment is feasible for the first levels of the Structural Health Monitoring (SHM) process, namely damage detection and arguably damage localization, this does not hold true for more advanced ... -
Optimal Dynamic Allocation of Collaborative Servers in Two Station Tandem Systems
(2019)We consider two-stage tandem queueing systems with one dedicated server in each station and a flexible server that can serve both stations. We assume exponential service times, linear holding costs accrued by jobs present ... -
Optimal server assignment in a two-stage tandem queueing system
(2020)We study Markovian queueing systems consisting of two stations in tandem. There is a dedicated server in each station and an additional server that can be assigned to any station. Assuming that linear holding costs are ... -
Performance evaluation of a production line operated under an echelon buffer policy
(2018)We consider a production line consisting of several machines in series separated by intermediate finite-capacity buffers. The line operates under an Echelon Buffer (EB) policy according to which each machine can store the ... -
Probability from chaos
(2004)Chaos is argued to lie at the origin of probability in nature. This is supported by several results: natural probabilistic processes are components of Markov processes; and Markov processes arise as projections of chaotic ...