Results 91 to 100 of about 404,944 (276)
Stochastic Processes and their ApplicationsThe Importance Markov chain is a novel algorithm bridging the gap between rejection sampling and importance sampling, moving from one to the other through a tuning parameter. Based on a modified sample of an instrumental Markov chain targeting an instrumental distribution (typically via a MCMC kernel), the Importance Markov chain produces an extended ...
Andral, Charly +3 more
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Business Strategy and the Environment, EarlyView.ABSTRACT The concept of predictive maintenance in advanced manufacturing systems is crucial from the point of view of resource efficiency in the era of high competitiveness forced by energy transformation in the digital economy. Against the backdrop of sustainability and the opportunities a data cooperative offers, the combination of predictive ...
Christian Schachtner +6 more
wiley +1 more source
Business Strategy and the Environment, EarlyView.ABSTRACT This article contributes to sustainability research by investigating the complex, geopolitically induced challenges faced by industrial supply chains under international sanctions. Using Iran's steel industry as a case, it examines sustainability barriers through the lens of stakeholder theory. A mixed methods approach was employed.
Seyed Hamed Moosavirad +2 more
wiley +1 more source
Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches
Chemistry – A European Journal, EarlyView.Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain +3 more
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MONTE CARLO MARKOV CHAIN (MCMC) STOCHASTIC MODELING OF SUPPLY CHAIN
Annals: Series on engineering sciences (Academy of Romanian Scientists)Effective inventory management in multi-echelon supply chains is challenged by stochastic demand and uncertain lead times, which amplify variability and increase operational costs.
Marcel ILIE, Augustin SEMENESCU
doaj +1 more source
Projection of Markov measures may be Gibbsian
, 2002 We study the induced measure obtained from a 1-step Markov measure, supported by a topological Markov chain, after the mapping of the original alphabet onto another one.
Chazottes, J. -R., Ugalde, E.
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The Canadian Journal of Chemical Engineering, EarlyView.Abstract Identification of firefighting strategies (i.e., which endangered units to suppress or cool first) in chemical and process plants falls under the domain of multi‐objective decision‐making (MODM), where not only the safety and integrity of the affected process plant but also the safety of on‐site and off‐site vulnerable targets matter.
Sina Khakzad, Nima Khakzad
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, 2022 A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
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A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control
The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao +2 more
wiley +1 more source
Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
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