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Stochastic Bounds For Some Stochastic Optimisation Problems
Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools, 2020We consider the path length optimisation for an acyclic graph model with discrete random delays attributed to the edges. This problem has been largely studied in the literature and the application of the stochastic ordering is not new. We aim here to give a comprehensible presentation of some kinds of bounds with a particular attention to their ...
Fourneau, Jean-Michel +2 more
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SSRN Electronic Journal, 2008 Given the importance of return volatility on a number of practical financial management decisions, the efforts to provide good real-time estimates and forecasts of current and future volatility have been extensive. The main framework used in this context involves stochastic volatility models.
Torben G. Andersen, Luca Benzoni
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Solving Stochastic Inverse Problems with Stochastic BayesFlow
2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2023Normalizing flows have gained increasing attention in the area of probabilistic modeling. For solving inverse problems, BayesFlow is a state-of-the-art Bayesian inference method based on normalizing flows. However, BayesFlow suffers from overfitting in many real-world scenarios.
Yi Zhang 0139, Lars Mikelsons
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CORES OF STOCHASTIC COOPERATIVE GAMES WITH STOCHASTIC ORDERS
International Game Theory Review, 2002In this paper we analyze cooperative games where the worth of a coalition is uncertain and the players only know their probability distribution. The novelty of our analysis is that the comparison among the uncertain values is done by stochastic orders among random variables.
Francisco R. Fernández +2 more
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Combining the Stochastic Counterpart and Stochastic Approximation Methods
Discrete Event Dynamic Systems, 1997Let \(\ell(v, \theta)=E_v\{L(Y,\theta)\}\) be the expected performance of a discrete event system (DES), where \(L\) is the sample performance driven by an input vector \(Y\) with a probability density function \(f(y, v)\) and \(\theta\) is a parameter of the sample performance.
Jean-Pierre Dussault +3 more
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Stochastic and Non-Stochastic Feature Selection
2017Feature selection has been applied in several areas of science and engineering for a long time. This kind of pre-processing is almost mandatory in problems with huge amounts of features which requires a very high computational cost and also may be handicapped very frequently with more than two classes and lot of instances.
Antonio J. Tallón-Ballesteros +2 more
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Stochastic Model Checking with Stochastic Comparison
2005This paper presents a stochastic comparison based method to check state formulas defined over Discrete Time Markov Reward Models. High-level specifications like stochastic Petri nets, Stochastic Automata Networks, Stochastic Process Algebras have been developed to construct large Markov models.
Nihal Pekergin, Sana Younès
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Improving Stochastic Model Checking with Stochastic Bounds
2005 Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops), 2006Stochastic model checking requires the computation of steady-state or transient distribution for finite or infinite Markov chains for the evaluation of some formulas implying probabilities. However the numerical analysis of Markov chains is much less efficient than the sophisticated algorithmic techniques such as MTBDD developed for the deterministic ...
Fourneau, Jean-Michel +2 more
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ACM Transactions on Database Systems, 2012
In many applications involving multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions for selecting one object that fits best their personal needs. As a key feature, the skyline in a multidimensional space provides the minimum set of candidates for such purposes by removing ...
Wenjie Zhang 0001 +4 more
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In many applications involving multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions for selecting one object that fits best their personal needs. As a key feature, the skyline in a multidimensional space provides the minimum set of candidates for such purposes by removing ...
Wenjie Zhang 0001 +4 more
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2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including non-convex and data-dependent regularizers, to be introduced and tested ...
James Gregson +4 more
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We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including non-convex and data-dependent regularizers, to be introduced and tested ...
James Gregson +4 more
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