This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propos...
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This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propose an approximate procedure based on discretizing both the possible probability values and the parameter representing the probability of change. The result is a method which is quite efficient in time and space (with a complexity directly related to the number of points used in the discretization) and providing very accurate predictions as well. These benefits are checked with a detailed comparison with other standard procedures based on variable size windows or forgetting rates. (C) 2019 Elsevier B.V. All rights reserved.
We propose a game-theoretic approach to generalizing the classical Schelling model. At the core of our model are two features that did not receive much attention before. First, we allow multiple individuals to occupy ...
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ISBN:
(纸本)9781450375184
We propose a game-theoretic approach to generalizing the classical Schelling model. At the core of our model are two features that did not receive much attention before. First, we allow multiple individuals to occupy the same location. Second, each individual's choice of location is influenced by their social network neighbors that also choose the same location. In addition, an individual's choice is influenced by others in the adjacent locations in a network-structured way, which captures the main spirit of the classical Schelling model and its numerous extensions. Our solution concept is a stable configuration represented as a pure-strategy Nash equilibrium (PSNE). We show that even for various special cases of the problem, computing or counting PSNE is provably hard. We give algorithms for computing PSNE, including efficient algorithms for several special cases. We highlight some of the attractive features of our model, such as predicting very few PSNE, through experiments.
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