Bayesian parameter estimation and error analysis
Bayesian Estimation of the Binomial Parameter
... (Asymptotic expansions of factorials can however be used if desired to evaluate posterior distributions approximately and, for samples where truncation of the poste[r] ...
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Bayesian estimation for the multifractality parameter
... Multifractal analysis has matured into a widely used signal and im- age processing ...rate estimation of multifractal parameters is very challenging in sit- uations where the sample size is small (notably ...
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A Bayesian parameter estimation approach to pulsar time-of-arrival analysis
... of Bayesian data analysis techniques to the problem of pulsar ...pulsar parameter estimates from a single simulated ...further analysis is required to rule out such effects as contributors to ...
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Predictive parameter estimation for Bayesian filtering
... The Kalman filter is an exact solution to the filtering problem on hidden Markov models, provided all distributions are Gaussian, all mean functions are linear, and[r] ...
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Reduced basis approximation and a posteriori error estimation for parametrized parabolic PDEs; Application to real-time Bayesian parameter estimation
... N . The SCM contribution to the Online cost is quite small and often negligible. POD-Greedy sampling strategy The sampling procedure for Navier-Stokes is very similar to the POD-Greedy sampling procedure for linear ...
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Bayesian Wavelet Estimation Of Long Memory Parameter
... This Regular Article is brought to you for free and open access by the Open Access Journals at DigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an ...
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Bayesian Estimation of Unknown Regression Error Heteroscedasticity
... a Bayesian procedure to estimate heteroscedastic variances of the regression error term, when the form of heteroscedasticity is ...a Bayesian that uses information obtained from the HCCM, in terms of ...
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Bayesian Estimation of Unknown Regression Error Heteroscedasticity
... 5 Concluding Remarks In this paper, we proposed a fully Bayesian method to estimate regression error term scedastic function ω without assuming any structure on it. One unique feature of our methodology is ...
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A Bayesian approach to parameter estimation for kernel density estimation via transformations
... density estimation of bivariate insurance claim data via ...the Bayesian sampling algorithm proposed by Zhang, King and Hyndman (2006) and present a Metropolis-Hastings sampling procedure to sample the ...
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Recursive Bayesian Methods For Sequential Parameter-State Estimation
... The primary problem in Bayesian inference is that of the computation of pos- terior distribution. It is a difficult quantity to compute because of the high dimen- sions involved. In this chapter, we develop ...
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Parameter estimation via conditional expectation: a Bayesian inversion
... As a first set of experiments we take the measurement operator to be linear in the state variable to be identified, i.e. we can observe the whole state directly. At the moment we consider updates after each day—whereas in ...
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Hybrid-Parallel Parameter Estimation for Frequentist and Bayesian Models
... use a distributed framework such as map-reduce to aggregate the computations [ Neiswanger et al. , 2015 ]. The second approach, is to exploit the underlying structure of the optimization problem to reduce the number of ...
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Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution
... obtain Bayesian estimators of the scale parameter of Nakagami ...this parameter, we consider Uniform prior, Inverse Exponential prior and Levy ...Squared Error Loss Function (SELF), Quadratic ...
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Constrained parameter estimation with uncertain priors for Bayesian networks
... in Bayesian statistical inference is to choose a class Γ of prior distributions and compute some quantity, such as the posterior risk, the Bayes risk or the posterior expected value, as the prior ranges over ...
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Bayesian parameter estimation and variable selection for quantile regression
... Despite these concerns, the AL distribution is easy to work with for applied re- searchers if the key aim is parameter estimation. In particular, as will be shown in the next section, the AL distribution ...
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A hierarchical Bayesian approach for parameter estimation in HIV models
... 4 Simulation Studies II: Subjects Undergoing Therapy In this section, we apply the proposed Bayesian approach to estimate the parameters of interest in the case that an STI strategy is adopted for the patient ...
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Sparse Bayesian blind image deconvolution with parameter estimation
... algorithm is very robust and that it is capable of restoring blurred images taken by a commercial camera very suc- cessfully under various non-parametric motion blurs. In addition, the proposed algorithm is very ...
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Parameter estimation for text analysis
... of Bayesian parameter esti- mation in the discrete domain, which is necessary to understand the inner workings of topic-based text analysis approaches like probabilistic latent semantic ...
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Bootstrap Specification Tests with Dependent Observations and Parameter Estimation Error
... of parameter estimation error does not enter the limiting distribution of the test statistic; and Inoue (1999) who studies di®erent conditional features, such as conditional symmetry and conditional ...
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Practical Aspects of the Equation-Error Method for Aircraft Parameter Estimation
... data analysis and ...The parameter estimation algorithm tries to account for the flight condition drift in the regressors by adjusting the bias ...
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