Search results for "Bay"
showing 10 items of 1187 documents
“Anti-Bayesian” parametric pattern classification using order statistics criteria for some members of the exponential family
2013
This paper submits a comprehensive report of the use of order statistics (OS) for parametric pattern recognition (PR) for various distributions within the exponential family. Although the field of parametric PR has been thoroughly studied for over five decades, the use of the OS of the distributions to achieve this has not been reported. The pioneering work on using OS for classification was presented earlier for the uniform distribution and for some members of the exponential family, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean. A…
Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods
2009
Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by m…
Generalized Bayesian pursuit: A novel scheme for multi-armed Bernoulli bandit problems
2011
Published version of a chapter in the book: IFIP Advances in Information and Communication Technology. Also available from the publisher at: http;//dx.doi.org/10.1007/978-3-642-23960-1_16 In the last decades, a myriad of approaches to the multi-armed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, while UCB-Tuned and the ε -greedy class of algorithms can be seen as state-of-the-art regret minimizing algorithms. Recently, however, the Bayesian Learning Automaton (BLA) outperformed all of these, and other schemes, in a wide range of experiments. Although seemingly incompatible, in…
Privacy Violation Classification of Snort Ruleset
2010
Published version of a paper presented at the 2010 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Paper also available from the publisher:http://dx.doi.org/10.1109/PDP.2010.87 It is important to analyse the privacy impact of Intrusion Detection System (IDS) rules, in order to understand a…
Consistency of Probability Decision Rules and Its Inference in Probability Decision Table
2012
In most synthesis evaluation systems and decision-making systems, data are represented by objects and attributes of objects with a degree of belief. Formally, these data can be abstracted by the form (objects; attributes; P), wherePrepresents a kind degree of belief between objects and attributes, such that,Pis a basic probability assignment. In the paper, we provide a kind of probability information system to describe these data and then employ rough sets theory to extract probability decision rules. By extension of Dempster-Shafer evidence theory, we can get probabilities of antecedents and conclusion of probability decision rules. Furthermore, we analyze the consistency of probability de…
Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore Game
2012
Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-012-0346-z The two-armed bandit problem is a classical optimization problem where a decision maker sequentially pulls one of two arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Bandit problems are particularly fascinating because a large class of real world problems, including routing, Quality of Service (QoS) control, game playing, and resource allocation, can be solved …
Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes
2010
Accepted version of an article published in the journal: Pattern Recognition. Published version on Sciverse: http://dx.doi.org/10.1016/j.patcog.2010.01.018 Linear dimensionality reduction (LDR) techniques have been increasingly important in pattern recognition (PR) due to the fact that they permit a relatively simple mapping of the problem onto a lower-dimensional subspace, leading to simple and computationally efficient classification strategies. Although the field has been well developed for the two-class problem, the corresponding issues encountered when dealing with multiple classes are far from trivial. In this paper, we argue that, as opposed to the traditional LDR multi-class schemes…
E-komercija un tās ietekme uz uzņēmējdarbību un patērētājiem.
2017
Bakalaura darba tēma ir „E-Komercija un tās ietekme uz patērētājiem un uzņēmējdarbību”. Darba kopejais apjoms ir 95 lapas, kas ietver 1 tabulu, 22 attēlus, kā arī sarakstu ar izmantoto literatūtu un citiem avotiem. Bakalaura darba mērķis ir, apkopojot un analizējot informāciju par interneta un ekomercijas vēsturi, attīstību un ietekmi uz patērētājiem, secināt, kādi ir Latvijas patērētāju ieradumi, lai sniegtu priekšlikumus efektīvākai e-komercijas integrācijai uzņēmējdarbībā un patērētāju ikdienā Bakalaura darbs sastāv no piecām nodaļām. Pirmajā nodaļā darba autori analizē ekomercijas būtību, izmantojot teorētiskos materiālus. Otrajā nodaļā tiek aplūkota e-komercijas ietekme uz sabiedrību u…
What drives German foreign direct investment? New evidence using Bayesian statistical techniques
2019
Abstract Despite the importance of Germany as an issuer of foreign direct investment (FDI), the studies analyzing its determinants are far from conclusive. This research contributes to filling this gap providing new evidence for the period 1996–2012. In order to reduce model uncertainty, we adopt a Bayesian model averaging (BMA) approach. We find that determinants associated with horizontal FDI appear to be dominant for explaining FDI in developed countries while for the group of developing countries covariates associated with vertical FDI motives play a larger role. Within Europe, while the majority of FDI is horizontally driven in “core” countries, in the “periphery” vertical motivations …
Ethics of Beliefs
2017
This paper deals with the concept of positive learning (PL). The main goal is to provide a working definition of PL on which further refinements and extensions can be based. First, I formulate a list of desiderata for a definition of PL: I argue that a working definition of PL should (i) make the involved epistemic norms explicit, (ii) be flexible, and (iii) be empirically tractable. After that, I argue that a working definition of PL should focus on three basic epistemic norms (which I call Evidentialism, Degrees of Plausibility, and Non-Arbitrary Updates). Drawing on work on the ethics of belief and Bayesian inference, I highlight theoretical and empirical challenges that already follow f…