Spelling suggestions: "subject:"model aselection"" "subject:"model selelection""
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Selecting Web Services by Problem SimilarityYan, Shih-hua 11 February 2009 (has links)
The recent development of the service-oriented architecture (SOA) has provided an opportunity to apply this new technology to support model management. This is particularly critical when more and more decision models are delivered as web services. A web-services-based approach to model management is useful in providing effective decision support.
When a decision model is implemented as a web service, it is called a model-based web service. In model management, selecting a proper model-based web service is an important issue. Most current research on selecting such web service relies on matching inputs and outputs of the model, which is oversimplified. The incorporation of more semantic knowledge may be necessary to make the selection of model-based web services more effective.
In this research, we propose a new mechanism that represents the semantics associated with a problem and then use the similarity of semantic information between a new problem description and existing web services to find the most suitable web services for solving the new problem. The paper defines the concept of entity similarity, attribute similarity, and functional similarity for problem matching. The web service that has the highest similarity is chosen as a base for constructing the new web services. The identified mapping is converted into BPEL4WS codes for utilizing the web services. To verify the feasibility of the proposed method, a prototype system has been implemented in JAVA.
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A Model of Positive Sequential Dependencies in Judgments of FrequencyAnnis, Jeffrey Scott 01 January 2013 (has links)
Positive sequential dependencies occur when the response on the current trial n is positively correlated with the response on trial n-1. This was recently observed in a Judgment of Frequency (JOF) task (Malmberg and Annis, 2011). A model of positive sequential dependencies was developed in the REM framework (Shiffrin & Steyvers, 1997) by assuming that features that represent the current test item in a retrieval cue carry over from the previous retrieval cue. To assess the model, we sought a set of data that allows us to distinguish between frequency similarity and item similarity. Therefore, we chose to use a JOF task in which we manipulated the item similarity of the stimuli by presenting either landscape photos (high similarity), or photos of everyday objects such as shoes, cars, etc (low similarity). Similarity was modeled by assuming either that the item representations share a proportion of features or by assuming that the exemplars from different stimulus classes vary in the distinctiveness or diagnosticity. The model fits indicated that the best way to model similarity was to assume that items share a proportions of features.
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Selection, calibration, and validation of coarse-grained models of atomistic systemsFarrell, Kathryn Anne 03 September 2015 (has links)
This dissertation examines the development of coarse-grained models of atomistic systems for the purpose of predicting target quantities of interest in the presence of uncertainties. It addresses fundamental questions in computational science and engineering concerning model selection, calibration, and validation processes that are used to construct predictive reduced order models through a unified Bayesian framework. This framework, enhanced with the concepts of information theory, sensitivity analysis, and Occam's Razor, provides a systematic means of constructing coarse-grained models suitable for use in a prediction scenario. The novel application of a general framework of statistical calibration and validation to molecular systems is presented. Atomistic models, which themselves contain uncertainties, are treated as the ground truth and provide data for the Bayesian updating of model parameters. The open problem of the selection of appropriate coarse-grained models is addressed through the powerful notion of Bayesian model plausibility. A new, adaptive algorithm for model validation is presented. The Occam-Plausibility ALgorithm (OPAL), so named for its adherence to Occam's Razor and the use of Bayesian model plausibilities, identifies, among a large set of models, the simplest model that passes the Bayesian validation tests, and may therefore be used to predict chosen quantities of interest. By discarding or ignoring unnecessarily complex models, this algorithm contains the potential to reduce computational expense with the systematic process of considering subsets of models, as well as the implementation of the prediction scenario with the simplest valid model. An application to the construction of a coarse-grained system of polyethylene is given to demonstrate the implementation of molecular modeling techniques; the process of Bayesian selection, calibration, and validation of reduced-order models; and OPAL. The potential of the Bayesian framework for the process of coarse graining and of OPAL as a means of determining a computationally conservative valid model is illustrated on the polyethylene example. / text
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Estimating and Correcting the Effects of Model Selection Uncertainty / Estimating and Correcting the Effects of Model Selection UncertaintyNguefack Tsague, Georges Lucioni Edison 03 February 2006 (has links)
No description available.
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Coupling distances between Lévy measures and applications to noise sensitivity of SDEGairing, Jan, Högele, Michael, Kosenkova, Tetiana, Kulik, Alexei January 2013 (has links)
We introduce the notion of coupling distances on the space of Lévy measures in order to quantify rates of convergence towards a limiting Lévy jump diffusion in terms of its characteristic triplet, in particular in terms of the tail of the Lévy measure. The main result yields an estimate of the Wasserstein-Kantorovich-Rubinstein distance on path space between two Lévy diffusions in terms of the couping distances. We want to apply this to obtain precise rates of convergence for Markov chain approximations and a statistical goodness-of-fit test for low-dimensional conceptual climate models with paleoclimatic data.
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An evaluation of latent Dirichlet allocation in the context of plant-pollinator networksCallaghan, Liam 08 January 2013 (has links)
There may be several mechanisms that drive observed interactions
between plants and pollinators in an ecosystem, many of which may
involve trait matching or trait complementarity. Hence a model of
insect species activity on plant species should be represented as
a mixture of these linkage rules. Unfortunately, ecologists do not
always know how many, or even which, traits are the main contributors
to the observed interactions. This thesis proposes the Latent Dirichlet
Allocation (LDA) model from artificial intelligence for modelling
the observed interactions in an ecosystem as a finite mixture of
(latent) interaction groups in which plant and pollinator pairs that
share common linkage rules are placed in the same interaction group.
Several model selection criteria are explored for estimating how many
interaction groups best describe the observed interactions. This thesis
also introduces a new model selection score called ``penalized perplexity".
The performance of the model selection criteria, and of LDA in general,
are evaluated through a comprehensive simulation study that consider
networks of various size along with varying levels of nesting and numbers of
interaction groups. Results of the simulation study suggest that LDA
works well on networks with mild-to-no nesting, but loses accuracy with
increased nestedness. Further, the penalized perplexity tended to
outperform the other model selection criteria in identifying the correct
number of interaction groups used to simulate the data. Finally, LDA was
demonstrated on a real network, the results of which provided insights
into the functional roles of pollinator species in the study region.
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Machine Learning Techniques for Large-Scale System ModelingLv, Jiaqing 31 August 2011 (has links)
This thesis is about some issues in system modeling: The first is a parsimonious
representation of MISO Hammerstein system, which is by projecting the multivariate
linear function into a univariate input function space. This leads to the so-called
semiparamtric Hammerstein model, which overcomes the commonly known “Curse
of dimensionality” for nonparametric estimation on MISO systems. The second issue
discussed in this thesis is orthogonal expansion analysis on a univariate Hammerstein
model and hypothesis testing for the structure of the nonlinear subsystem. The generalization
of this technique can be used to test the validity for parametric assumptions
of the nonlinear function in Hammersteim models. It can also be applied to approximate
a general nonlinear function by a certain class of parametric function in the
Hammerstein models. These techniques can also be extended to other block-oriented
systems, e.g, Wiener systems, with slight modification. The third issue in this thesis is
applying machine learning and system modeling techniques to transient stability studies
in power engineering. The simultaneous variable section and estimation lead to a
substantially reduced complexity and yet possesses a stronger prediction power than
techniques known in the power engineering literature so far.
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Bayesian latent class metric conjoint analysis. A case study from the Austrian mineral water market.Otter, Thomas, Tüchler, Regina, Frühwirth-Schnatter, Sylvia January 2002 (has links) (PDF)
This paper presents the fully Bayesian analysis of the latent class model using a new approach towards MCMC estimation in the context of mixture models. The approach starts with estimating unidentified models for various numbers of classes. Exact Bayes' factors are computed by the bridge sampling estimator to compare different models and select the number of classes. Estimation of the unidentified model is carried out using the random permutation sampler. From the unidentified model estimates for model parameters that are not class specific are derived. Then, the exploration of the MCMC output from the unconstrained model yields suitable identifiability constraints. Finally, the constrained version of the permutation sampler is used to estimate group specific parameters. Conjoint data from the Austrian mineral water market serve to illustrate the method. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Machine Learning Techniques for Large-Scale System ModelingLv, Jiaqing 31 August 2011 (has links)
This thesis is about some issues in system modeling: The first is a parsimonious
representation of MISO Hammerstein system, which is by projecting the multivariate
linear function into a univariate input function space. This leads to the so-called
semiparamtric Hammerstein model, which overcomes the commonly known “Curse
of dimensionality” for nonparametric estimation on MISO systems. The second issue
discussed in this thesis is orthogonal expansion analysis on a univariate Hammerstein
model and hypothesis testing for the structure of the nonlinear subsystem. The generalization
of this technique can be used to test the validity for parametric assumptions
of the nonlinear function in Hammersteim models. It can also be applied to approximate
a general nonlinear function by a certain class of parametric function in the
Hammerstein models. These techniques can also be extended to other block-oriented
systems, e.g, Wiener systems, with slight modification. The third issue in this thesis is
applying machine learning and system modeling techniques to transient stability studies
in power engineering. The simultaneous variable section and estimation lead to a
substantially reduced complexity and yet possesses a stronger prediction power than
techniques known in the power engineering literature so far.
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Bayesian Analysis of Spatial Point PatternsLeininger, Thomas Jeffrey January 2014 (has links)
<p>We explore the posterior inference available for Bayesian spatial point process models. In the literature, discussion of such models is usually focused on model fitting and rejecting complete spatial randomness, with model diagnostics and posterior inference often left as an afterthought. Posterior predictive point patterns are shown to be useful in performing model diagnostics and model selection, as well as providing a wide array of posterior model summaries. We prescribe Bayesian residuals and methods for cross-validation and model selection for Poisson processes, log-Gaussian Cox processes, Gibbs processes, and cluster processes. These novel approaches are demonstrated using existing datasets and simulation studies.</p> / Dissertation
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