Bayesian methods for parameter estimation
Using Bayesian methods for the parameter estimation of deformation monitoring networks
... observational methods in all fields of geosciences have changed ...towards Bayesian statistics, which has advantages in complex problems and better re- flects the way scientists think about ...the ...
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Parameter Estimation with Mixture Item Response Theory Models: A Monte Carlo Comparison of Maximum Likelihood and Bayesian Methods
... Conversely, the MCMC approach is often very time consuming to implement (sometimes taking 10 days or more to fit a single model), and may encounter difficulties in converging to solutions for individual parameters (Li, ...
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Computational Bayesian Methods for Insurance Premium Estimation
... propose methods to compute the premium risk loading using ...work Bayesian statistics are used for parameter ...of Bayesian estimates more natural than the frequentist ...to Bayesian ...
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Recursive Bayesian Methods For Sequential Parameter-State Estimation
... in Bayesian inference is that of the computation of pos- terior ...efficient methods based on the concept and techniques of Variational Bayes (VB) ...
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Bayesian Wavelet Estimation Of Long Memory Parameter
... Statistical Methods by an authorized editor of ...Wavelet Estimation Of Long Memory Parameter," Journal of Modern Applied Statistical Methods: ...
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A Comparison between Maximum Likelihood and Bayesian Estimation Methods for a Shape Parameter of the Weibull-Exponential Distribution
... the Bayesian analysis of a shape parameter of the Weibull-Exponential distribution in this ...decreasing parameter values while the use of precautionary loss function can be preferred for increasing ...
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A Bayesian approach to parameter estimation for kernel density estimation via transformations
... transformation methods that have been studied in the ...transformation methods are particularly useful with insurance data because the distri- butions of insurance claim data are often skewed and present ...
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Bayesian parameter estimation and variable selection for quantile regression
... sampling methods are usually required as analytical expressions for posterior summaries are generally unavailable in closed form regardless of the prior ...
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A hierarchical Bayesian approach for parameter estimation in HIV models
... In this paper, we report on our investigation of how MCMC based methods will perform on estimation in complex models for HIV progression in untreated patients as well as in patients undergoing STI therapy. ...
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Constrained parameter estimation with uncertain priors for Bayesian networks
... This paper is organized as follows: In Section 2, we introduce some prelimi- naries. Section 3 is devoted to simultaneous Bayes and the idea of CB learning. In addition, explicit forms for parameter estimates are ...
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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 via conditional expectation: a Bayesian inversion
... equation. Methods on how to transform the prior RV into the one which is conditioned on the observation will be discussed as well ...(MCMC) methods to sample from the posterior (see ...
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Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach
... these methods to actual field data, where the structural inadequacies of the models, measurement errors and the assumptions inherent to the petrophysical relationship complicate the inversion problem (Kowalsky et ...
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A Bayesian parameter estimation approach to pulsar time-of-arrival analysis
... In addition we hope to be able to include, and account for, many of the physical effects and data analysis issues that we have ignored in our toy model approach. These include a more robust treatment of the noise where ...
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Comparison of Two Priors in Bayesian Estimation for Parameter of Weibull Distribution
... Bayes estimation method analitically and using simulation study (Muharisa et ...Bayes methods including invers Gamma distribution as conjugate prior and Jeffreys’ method as non-informative ...
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Some Bayesian methods for univariate density estimation
... density estimation is, there are numerous ways to approach the problem from a non-Bayesian or Bayesian ...a Bayesian approach with priors on the distribution’s parameters are common ...
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Modeling and Bayesian parameter estimation for shape memory alloy bending actuators
... ABSTRACT In this paper, we employ a homogenized energy model (HEM) for shape memory alloy (SMA) bending actuators. Additionally, we utilize a Bayesian method for quantifying parameter uncertainty. The ...
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Parameter Estimation Risk in Asset Pricing and Risk Management: A Bayesian Approach
... on parameter values for many problems encountered in finance. Bayesian MCMC techniques provide an elegant solution to handle these problems that would be very cumbersome ...standard methods of ...
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Bayesian Parameter Estimation for the Localization of a Radioactive Source in a Heterogeneous Urban Environment.
... proposes methods for dealing with the spatial errors present in measurements recorded by a detector network, as well as for variations in the efficiency of individual ...
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Bayesian Estimations with Fuzzy Data to Estimation Inverse Rayleigh Scale Parameter
... scale parameter of IRD for all sample sizes and with the cases λ = ...the parameter of the IRD and also the parameter can be estimated by other ...
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