Search results for " Probability"
showing 10 items of 2176 documents
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
Human Factor Interrelationships to Improve Worker Reliability: Implementation of MCDM in the Agri-Food Sector
2023
Performance Shaping Factors (PSFs) are contextual, individual, and cognitive factors used in Human Reliability Analysis (HRA) to quantify the worker contribution to errors when performing a generic task. Although the empirical evidence demonstrates the existence of PSF interrelationships, the majority of HRA methods assume their independence. As a consequence, the resulting Human Error Probability (HEP) might be over- or underestimated. To deal with this issue, only a few qualitative guidelines or statistical-based approaches have been proposed so far. While the former are not well structured, the latter require a high computational effort and a proper number of input data. Therefore, the p…
Cell Association With Load Balancing in Nonuniform Heterogeneous Cellular Networks: Coverage Probability and Rate Analysis
2017
To meet the ever-growing traffic demand and address the cell capacity shortage problem, associating end users to various tiers of cells in a multitier cellular network appears to be a promising approach. In this paper, we consider a nonuniform heterogeneous cellular network (NuHCN) and propose a cell association scheme that selectively mutes certain small-cell base stations (BSs) and covers end users by cell range extension (via cell biasing) for achieving load balancing. The envisaged NuHCN is comprised of two tiers of BSs, i.e., macro- and small-cell BSs, deployed according to three independent homogeneous Poisson point processes for BSs and end users, respectively. Accordingly, the avail…
A saturated strategy robustly ensures stability of the cooperative equilibrium for Prisoner's dilemma
2016
We study diffusion of cooperation in a two-population game in continuous time. At each instant, the game involves two random individuals, one from each population. The game has the structure of a Prisoner's dilemma where each player can choose either to cooperate (c) or to defect (d), and is reframed within the field of approachability in two-player repeated game with vector payoffs. We turn the game into a dynamical system, which is positive, and propose a saturated strategy that ensures local asymptotic stability of the equilibrium (c, c) for any possible choice of the payoff matrix. We show that there exists a rectangle, in the space of payoffs, which is positively invariant for the syst…
The shape of small sample biases in pricing kernel estimations
2016
AbstractNumerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In the first step, we analyse theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader regarding potential computational issues coupled with statistical techniques. In the second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that est…
Recursive and bargaining values
2021
Abstract We introduce two families of values for TU-games: the recursive and bargaining values. Bargaining values are obtained as the equilibrium payoffs of the symmetric non-cooperative bargaining game proposed by Hart and Mas-Colell (1996). We show that bargaining values have a recursive structure in their definition, and we call this property recursiveness. All efficient, linear, and symmetric values that satisfy recursiveness are called recursive values. We generalize the notions of potential, and balanced contributions property, to characterize the family of recursive values. Finally, we show that if a time discount factor is considered in the bargaining model, every bargaining value h…
Values of games with probabilistic graphs
1999
Abstract In this paper we consider games with probabilistic graphs. The model we develop is an extension of the model of games with communication restrictions by Myerson (1977) . In the Myerson model each pair of players is joined by a link in the graph if and only if these two players can communicate directly. The current paper considers a more general setting in which each pair of players has some probability of direct communication. The value is defined and characterized in this context. It is a natural extension of the Myerson value and it turns out to be the Shapley value of a modified game.
REPEATED GAMES WITH PROBABILISTIC HORIZON
2005
Repeated games with probabilistic horizon are defined as those games where players have a common probability structure over the length of the game's repetition, T. In particular, for each t, they assign a probability pt to the event that "the game ends in period t". In this framework we analyze Generalized Prisoners' Dilemma games in both finite stage and differentiable stage games. Our construction shows that it is possible to reach cooperative equilibria under some conditions on the distribution of the discrete random variable T even if the expected length of the game is finite. More precisely, we completely characterize the existence of sub-game perfect cooperative equilibria in finite s…
Thompson Sampling for Dynamic Multi-armed Bandits
2011
The importance of multi-armed bandit (MAB) problems is on the rise due to their recent application in a large variety of areas such as online advertising, news article selection, wireless networks, and medicinal trials, to name a few. The most common assumption made when solving such MAB problems is that the unknown reward probability theta k of each bandit arm k is fixed. However, this assumption rarely holds in practice simply because real-life problems often involve underlying processes that are dynamically evolving. In this paper, we model problems where reward probabilities theta k are drifting, and introduce a new method called Dynamic Thompson Sampling (DTS) that facilitates Order St…
Learning spatial filters for multispectral image segmentation.
2010
International audience; We present a novel filtering method for multispectral satel- lite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments car- ried out on multiclass one-against-all classification and tar- get detection show the capabilities of the learned spatial fil- ters.