17 research outputs found
Effects of customer trust and online experiences in building hospitality brands
Customer trust embodies customer beliefs of actually receiving a promised service and manifestations of consumer’s confidences in an exchange parties reliability and integrity. The study is based on the fact as to how trusts criteria affect online purchase especially in regard to booking and buying the accommodations and also that accommodation providers assume that are very essential for consumers to make the online purchase. In total 150 consumers and 80 hotels owners/operators in India were examined. There are enormous discrepancies between consumers and accommodation providers were searched. Like formal guarantee of providers, security concern, refund of price paid delivery time and information about confirmation and they will switch from one brand to other due to promise breakage, less service quality, high price charged. However, these trust criteria were viewed inconsequential by the accommodation providers. It concluded with vast number of suggestions and recommendations for the accommodation providers need to include in their websites and build reputation and strong brands in the hospitality market
Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data
The anomaly detection method presented by this paper has a special feature:
it does not only indicate whether an observation is anomalous or not but also
tells what exactly makes an anomalous observation unusual. Hence, it provides
support to localize the reason of the anomaly.
The proposed approach is model-based; it relies on the multivariate
probability distribution associated with the observations. Since the rare
events are present in the tails of the probability distributions, we use copula
functions, that are able to model the fat-tailed distributions well. The
presented procedure scales well; it can cope with a large number of
high-dimensional samples. Furthermore, our procedure can cope with missing
values, too, which occur frequently in high-dimensional data sets.
In the second part of the paper, we demonstrate the usability of the method
through a case study, where we analyze a large data set consisting of the
performance counters of a real mobile telecommunication network. Since such
networks are complex systems, the signs of sub-optimal operation can remain
hidden for a potentially long time. With the proposed procedure, many such
hidden issues can be isolated and indicated to the network operator.Comment: 27 pages, 12 figures, accepted at ACM Transactions on Intelligent
Systems and Technolog
Discovering a junction tree behind a Markov network by a greedy algorithm
In an earlier paper we introduced a special kind of k-width junction tree,
called k-th order t-cherry junction tree in order to approximate a joint
probability distribution. The approximation is the best if the Kullback-Leibler
divergence between the true joint probability distribution and the
approximating one is minimal. Finding the best approximating k-width junction
tree is NP-complete if k>2. In our earlier paper we also proved that the best
approximating k-width junction tree can be embedded into a k-th order t-cherry
junction tree. We introduce a greedy algorithm resulting very good
approximations in reasonable computing time.
In this paper we prove that if the Markov network underlying fullfills some
requirements then our greedy algorithm is able to find the true probability
distribution or its best approximation in the family of the k-th order t-cherry
tree probability distributions. Our algorithm uses just the k-th order marginal
probability distributions as input.
We compare the results of the greedy algorithm proposed in this paper with
the greedy algorithm proposed by Malvestuto in 1991.Comment: The paper was presented at VOCAL 2010 in Veszprem, Hungar
Abstracts from the 20th International Symposium on Signal Transduction at the Blood-Brain Barriers
https://deepblue.lib.umich.edu/bitstream/2027.42/138963/1/12987_2017_Article_71.pd
On the use of the copulas in characterizing the dependence on entropy
In this paper, a method for characterizing the dependence between two random variables is presented with the help of information theory. There are several well-known methods that describe the stochastic dependence. Some of these methods are based on the copula approach. The copula function is capable to exhibit the type of the dependence between two or more random variables.A method is proposed to characterize the dependence that uses certain entropy coefficients, which are calculated with the copula function associated to the joint distribution function
Matrix and graph representations of vine copula structures
Vine copulas can efficiently model a large portion of probability
distributions. This paper focuses on a more thorough understanding of their
structures. We are building on well-known existing constructions to represent
vine copulas with graphs as well as matrices. The graph representations include
the regular, cherry and chordal graph sequence structures, which we show
equivalence between. Importantly we also show that when a perfect elimination
ordering of a vine structure is given, then it can always be uniquely
represented with a matrix. O. M. N\'apoles has shown a way to represent them in
a matrix, and we algorithmify this previous approach, while also showing a new
method for constructing such a matrix, through cherry tree sequences. Lastly,
we prove that these two matrix-building algorithms are equivalent if the same
perfect elimination ordering is being used.Comment: 20 pages, 26 figure