Search results for "Bayes"
showing 10 items of 847 documents
Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach
2015
Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called "Exact Potent…
Convex semi-infinite games
1986
This paper introduces a generalization of semi-infinite games. The pure strategies for player I involve choosing one function from an infinite family of convex functions, while the set of mixed strategies for player II is a closed convex setC inRn. The minimax theorem applies under a condition which limits the directions of recession ofC. Player II always has optimal strategies. These are shown to exist for player I also if a certain infinite system verifies the property of Farkas-Minkowski. The paper also studies certain conditions that guarantee the finiteness of the value of the game and the existence of optimal pure strategies for player I.
Consensus in inventory games
2008
This paper studies design, convergence, stability and optimality of a distributed consensus protocol for n-player repeated non cooperative games under incomplete information. Information available to each player concerning the other players' strategies evolves in time. At each stage (time period), the players select myopically their best binary strategy on the basis of a payoff, defined on a single stage, monotonically decreasing with the number of active players. The game is specialized to an inventory application, where fixed costs are shared among all retailers, interested in reordering or not from a common warehouse. As information evolves in time, the number of active players changes t…
Heterogeneous network games: Conflicting preferences
2013
Proceeding at: 2nd Annual UECE Lisbon Meeting: Game Theory and Applications, took place 2010, November, 4-6, in Lisbon (Portugal). The event Web site http://pascal.iseg.utl.pt/~uece/lisbonmeetings2010/ In many economic situations, a player pursues coordination or anti-coordination with her neighbors on a network, but she also has intrinsic preferences among the available options. We here introduce a model which allows to analyze this issue by means of a simple framework in which players endowed with an idiosyncratic identity interact on a social network through strategic complements or substitutes. We classify the possible types of Nash equilibria under complete information, finding two thr…
Noncooperative dynamic games for inventory applications: A consensus approach
2008
We focus on a finite horizon noncooperative dynamic game where the stage cost of a single player associated to a decision is a monotonically nonincreasing function of the total number of players making the same decision. For the single-stage version of the game, we characterize Nash equilibria and derive a consensus protocol that makes the players converge to the unique Pareto optimal Nash equilibrium. Such an equilibrium guarantees the interests of the players and is also social optimal in the set of Nash equilibria. For the multi-stage version of the game, we present an algorithm that converges to Nash equilibria, unfortunately not necessarily Pareto optimal. The algorithm returns a seque…
Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models
2017
Topic models for text analysis are most commonly trained using either Gibbs sampling or variational Bayes. Recently, hybrid variational-Gibbs algorithms have been found to combine the best of both worlds. Variational algorithms are fast to converge and more efficient for inference on new documents. Gibbs sampling enables sparse updates since each token is only associated with one topic instead of a distribution over all topics. Additionally, Gibbs sampling is unbiased. Although Gibbs sampling takes longer to converge, it is guaranteed to arrive at the true posterior after infinitely many iterations. By combining the two methods it is possible to reduce the bias of variational methods while …
Ultimate Order Statistics-Based Prototype Reduction Schemes
2013
Published version of a chapter in the book: AI 2013: Advances in Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-319-03680-9_42 The objective of Prototype Reduction Schemes (PRSs) and Border Identification (BI) algorithms is to reduce the number of training vectors, while simultaneously attempting to guarantee that the classifier built on the reduced design set performs as well, or nearly as well, as the classifier built on the original design set. In this paper, we shall push the limit on the field of PRSs to see if we can obtain a classification accuracy comparable to the optimal, by condensing the information in the data set into a single tr…
Feature Selection for Ensembles of Simple Bayesian Classifiers
2002
A popular method for creating an accurate classifier from a set of training data is to train several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. However, the simple Bayesian classifier has much broader applicability than previously thought. Besides its high classification accuracy, it also has advantages in terms of simplicity, learning speed, classification speed, storage space, and incrementality. One way to generate an ensemble of simple Bayesian classifiers is to use different feature subsets as in the random subspace method. In this paper we present a technique for building ensembles o…
Suitability of chloroplast LSU rDNA and its diverse group I introns for species recognition and phylogenetic analyses of lichen-forming Trebouxia alg…
2009
To date, species identification of lichen photobionts has been performed principally on the basis of microscopic examinations and molecular data from nuclear-encoded genes. In plants, the chloroplast genome has been more readily exploited than the nuclear genome for systematic investigations. At the present time, very little information is available about the chloroplast genome of lichen-forming algae. For this reason, we have sequenced a portion of the gene encoding for the chloroplast large sub-unit rRNA (LSU rDNA) as a new molecular marker. Sequencing of the chloroplast LSU rDNAs revealed the existence of an unusual diversity of group I introns (a total of 31) within 15 analyzed Trebouxi…
Propuesta bayesiana para la elaboración de mapas genéticos
2016
En los últimos años, los avances en la investigación biotecnológica, permiten manipular genes o grupos de genes de forma específica en el genoma de los organismos vegetales para producir cultivos con mejores características. Su objetivo es la obtención de especies mejoradas con peculiaridades deseables como un crecimiento más rápido, capaces de una adaptación al medio en el que se desarrollan, más resistentes a plagas o a enfermedades o simplemente proporcionando frutos más grandes, uniformes y de mejor calidad. La investigación y el desarrollo sobre los marcadores moleculares está contribuyendo a la comprensión de los resultados derivados de la herencia de caracteres cuantitativos y a una …