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- W2012028249 abstract "We present a Bayesian Monte Carlo Markov Chain method to simultaneously estimate the spectral index and power amplitude of a fractional differenced Gaussian process at low frequency, in presence of white noise, and a linear trend and periodic signals. This method provides a sample of the likelihood function and thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. We test this method with simulated and real Global Positioning System height time series and propose it as an alternative to optimization methods currently in use. Furthermore, without any mathematical proof, the results from the simulations suggest that this method is unaffected by the stationary regime and hence, can be used to check whether or not a time series is stationary." @default.
- W2012028249 created "2016-06-24" @default.
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- W2012028249 date "2013-05-01" @default.
- W2012028249 modified "2023-10-17" @default.
- W2012028249 title "A Bayesian Monte Carlo Markov Chain Method for Parameter Estimation of Fractional Differenced Gaussian Processes" @default.
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- W2012028249 doi "https://doi.org/10.1109/tsp.2013.2245658" @default.
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