Search results for "STATISTICS"
showing 10 items of 7671 documents
A family of experiments to generate graphical user interfaces from BPMN models with stereotypes
2021
Abstract Context: A significant gap separates Business Process Model and Notation (BPMN) models representing processes from the design of Graphical User Interfaces (GUIs). Objective: This paper reports on a family of experiments to validate a method to automatically generate GUIs from BPMN models using stereotypes complemented with UML class primitives, and transformation rules. Method: We conducted two replications (23 and 31 subjects respectively) in which we compared two methods to generate GUIs from BPMN models; one automatic (using Stereotyped BPMN models) and one manual (using Non-stereotyped BPMN models). The study focuses on comparing effort, accuracy, and satisfaction (in terms of …
Importance sampling for Lambda-coalescents in the infinitely many sites model
2011
We present and discuss new importance sampling schemes for the approximate computation of the sample probability of observed genetic types in the infinitely many sites model from population genetics. More specifically, we extend the 'classical framework', where genealogies are assumed to be governed by Kingman's coalescent, to the more general class of Lambda-coalescents and develop further Hobolth et. al.'s (2008) idea of deriving importance sampling schemes based on 'compressed genetrees'. The resulting schemes extend earlier work by Griffiths and Tavar\'e (1994), Stephens and Donnelly (2000), Birkner and Blath (2008) and Hobolth et. al. (2008). We conclude with a performance comparison o…
Positive solutions for singular double phase problems
2021
Abstract We study the existence of positive solutions for a class of double phase Dirichlet equations which have the combined effects of a singular term and of a parametric superlinear term. The differential operator of the equation is the sum of a p-Laplacian and of a weighted q-Laplacian ( q p ) with discontinuous weight. Using the Nehari method, we show that for all small values of the parameter λ > 0 , the equation has at least two positive solutions.
Filtering design for two-dimensional Markovian jump systems with state-delays and deficient mode information
2014
This paper is concerned with the problem of H"~ filtering for a class of two-dimensional Markovian jump linear systems described by the Fornasini-Marchesini local state-space model. The systems under consideration are subject to state-delays and deficient mode information in the Markov chain. The description of deficient mode information is comprehensive that simultaneously includes the exactly known, partially unknown and uncertain transition probabilities. By invoking the properties of the transition probability matrix, together with the convexification of uncertain domains, a new H"~ performance analysis criterion for the filtering error system is firstly derived. Then, via some matrix i…
Model approximation for two-dimensional Markovian jump systems with state-delays and imperfect mode information
2014
Published version of an article in the journal: Multidimensional Systems and Signal Processing. Also available from the publisher at: http://dx.doi.org/10.1007/s11045-013-0276-x This paper is concerned with the problem of {Mathematical expression} model approximation for a class of two-dimensional (2-D) discrete-time Markovian jump linear systems with state-delays and imperfect mode information. The 2-D system is described by the well-known Fornasini-Marchesini local state-space model, and the imperfect mode information in the Markov chain simultaneously involves the exactly known, partially unknown and uncertain transition probabilities. By using the characteristics of the transition proba…
Approximations in Statistics from a Decision-Theoretical Viewpoint
1987
The approximation of the probability density p(.) of a random vector x∊X by another (possibly more convenient) probability density q(.) which belongs to a certain class Q is analyzed as a decision problem where the action space is the class Qof available approximations, the relevant uncertain event is the actual value of the vector x and the utility function is a proper scoring rule. The logarithmic divergence is shown to play a rather special role within this approach. The argument lies entirely within a Bayesian framework.
Separation conditions on controlled Moran constructions
2017
It is well known that the open set condition and the positivity of the $t$-dimensional Hausdorff measure are equivalent on self-similar sets, where $t$ is the zero of the topological pressure. We prove an analogous result for a class of Moran constructions and we study different kinds of Moran constructions with this respect.
Pattern classification using a new border identification paradigm: The nearest border technique
2015
Abstract There are many paradigms for pattern classification such as the optimal Bayesian, kernel-based methods, inter-class border identification schemes, nearest neighbor methods, nearest centroid methods, among others. As opposed to these, this paper pioneers a new paradigm, which we shall refer to as the nearest border (NB) paradigm. The philosophy for developing such a NB strategy is as follows: given the training data set for each class, we shall attempt to create borders for each individual class. However, unlike the traditional border identification (BI) methods, we do not undertake this by using inter-class criteria; rather, we attempt to obtain the border for a specific class in t…
Transience versus recurrence for scale-free spatial networks
2020
Weight-dependent random connection graphs are a class of local network models that combine scale-free degree distribution, small-world properties and clustering. In this paper we discuss recurrence or transience of these graphs, features that are relevant for the performance of search and information diffusion algorithms on the network.
A new paradigm for pattern classification: Nearest Border Techniques
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_44 There are many paradigms for pattern classification. As opposed to these, this paper introduces a paradigm that has not been reported in the literature earlier, which we shall refer to as the Nearest Border (NB) paradigm. The philosophy for developing such a NB strategy is as follows: Given the training data set for each class, we shall first attempt to create borders for each individual class. After that, we advocate that testing is accomplished by assigning the test sample to the class whose border it lies closest to…