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The ultimate product of distributed sensing is normally a model that describes the data or a set of processes for which the data is an observation. The sensor network itself affects the collected data, often due to the need to conserve deployment costs, including energy provisioning. These effects include data compression and transmission censoring in addition to the typical noise and distortion of...
Bayesian Networks (BNs) are used in a wide range of applications, being the representation of regulatory networks a recurrent one. Nowadays great interest is dedicated to the problem of inferring the network's structure solely from the data. Aiming more precise results, the inclusion of extra knowledge in the inference process has been already suggested, as well as a Bayesian coupling scheme for learning...
The task of finding transcription start sites (TSSs) can be modeled as a classification problem. Relevance vector machines (RVM) is a family of machine learning methods that represent a Bayesian approach to the training of general linear models (GLM). Based on the Markov-chain Monte Carlo(MCMC) sampler, propose a model for using the RVM to explore very large numbers of candidate features. The model...
In a Bayesian approach, we compare the volatility forecasting ability of ARCH, GARCH and stochastic volatility(SV) models, using daily Tehran stock market exchange data(TSE). To estimate the parameters of the models, Markov chain Monte Carlo(MCMC) methods is applied. The results show that the SV models perform better than the ARCH and GARCH family.
This paper presents a new approach to do reliability analysis for complex system, where a certain fraction of the subsystems is defined as a ??cure fraction?? under the consideration that such subsystems are ??longevous?? compared with the entire system. Including introducing environment covariates and the joint power prior, the proposed model is developed with the Bayesian survival analysis method,...
In this paper, the authors propose a new stochastic model, SVDJ model which has allowed for jump risk, to describe the dynamics behavior of spot exchange rate, and develop a MCMC (Markov chain Monte Carlo) method for the estimation of parameters, jumps, and volatility. Our empirical results indicate the significant existence of jumps in exchange rate process, and the incorporation of jump risk in...
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