In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propo...
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In this paper, we develop an option valuation model in the context of a discrete-time multivariate Markov chain model using the Esscher transform. The multivariate Markov chain provides a flexible way to incorporate t...
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Probabilistic Boolean Networks (PBNs) are useful models for modeling genetic regulatory networks. In this paper, we propose efficient algorithms for constructing a sparse probabilistic Boolean network when its transit...
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Probabilistic Boolean Networks (PBNs) are useful models for modeling genetic regulatory networks. In this paper, we propose efficient algorithms for constructing a sparse probabilistic Boolean network when its transition probability matrix and a set of possible Boolean networks are given. This is an interesting inverse problem in network inference and it is important in the sense that most microarray data sets are assumed to be obtained from sampling the steady-state.
In this paper, we study the problem of constructing a regulatory network of yeast in oxidative stress process. Discrete Dynamic System (DDS) model has been introduced in describing Gene Regulatory Networks (GRNs). How...
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In this paper, we study the problem of constructing a regulatory network of yeast in oxidative stress process. Discrete Dynamic System (DDS) model has been introduced in describing Gene Regulatory Networks (GRNs). However, delay effect was not taken into consideration within the model. A Time-delay DDS model composed of linear difference equations is developed to represent temporal interactions among significantly expressed genes. Interpolation and re-sampling are imposed to equalize the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed gene network using linear multiple regression has a very good match with the original data. Simulation results are given to demonstrate the effectiveness of our proposed model.
In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we p...
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ISBN:
(纸本)9781424468126;9780769540306
In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM.
This paper studied a multiple-stage tandem production system. The control strategy is Hybrid which combines Kanban and CONWIP strategy together. The objective here is to evaluate the performance of the system by Marko...
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This paper studied a multiple-stage tandem production system. The control strategy is Hybrid which combines Kanban and CONWIP strategy together. The objective here is to evaluate the performance of the system by Markov chain models. We focus on Work-In-Process (WIP) and lost rate of the system. In general it is hard to obtain an exact analytical result of the system. Thus some approximation methods are proposed here. A simple situation is considered and formulated as a Markov chain process. Then we derive the steady-state probability of the system for two and three machines case, and obtain the WIP and the lost rate. The formulations proposed show the connection of control cards and performance measures. The method can extended to the case of multiple machines. We also present the results of a simulation study that tests the performance of our approach.
In this paper, we develop an option valuation model in the context of a discrete-time multivariate Markov chain model using the Esscher transform. The multivariate Markov chain provides a flexible way to incorporate t...
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ISBN:
(纸本)9781424468126;9780769540306
In this paper, we develop an option valuation model in the context of a discrete-time multivariate Markov chain model using the Esscher transform. The multivariate Markov chain provides a flexible way to incorporate the dependency of the underlying asset price processes and price multi-state options written on several dependent underlying assets. In our model, the price of an individual asset can take finitely many values. The market described by our model is incomplete in general, hence there are more than one equivalent martingale pricing measures. We adopt conditional Esscher transform to determine an equivalent martingale measure for option valuation. We also document consequences for option prices of the dependency of the underlying asset prices described by the multivariate Markov chain model.
We demonstrate that high numerical aperture coherent diffractive imaging enables three-dimensional imaging from a single exposure angle under certain conditions. We demonstrate this technique, called ankylography, wit...
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In this paper, we study the problem of modeling the dependence of defaults in different sectors. We consider multiple default data sequences as a network and model them by using a Markov chain model. The new network m...
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In this paper, we study the problem of modeling the dependence of defaults in different sectors. We consider multiple default data sequences as a network and model them by using a Markov chain model. The new network model allows us to compute two important risk measures, namely, Value-at-Risk (VaR) and Expected Shortfall (ES). Numerical experiments are given to illustrate the practical implementation of the model. We also perform empirical studies of the model using real default data sequences and analyze the empirical behaviors of the risk measures arising from the model.
We demonstrate that high numerical aperture coherent diffractive imaging enables three-dimensional imaging from a single exposure angle under certain conditions. We demonstrate this technique, called ankylography, wit...
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ISBN:
(纸本)9781557528896
We demonstrate that high numerical aperture coherent diffractive imaging enables three-dimensional imaging from a single exposure angle under certain conditions. We demonstrate this technique, called ankylography, with simulations and experiment from a tabletop soft-x-ray laser.
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