A maximum likelihood method is introduced to estimate the burst-merging kernel from time series data, tested on synthetic models and applied to empirical examples.
Once the ordinal burst tree is generated, we randomly draw n − 1 IETs from the power-law IET distribution as P (τ) = τ −α Pτc τ =1 τ −α for τ = 1
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Maximum likelihood estimation of burst-merging kernels for bursty time series
A maximum likelihood method is introduced to estimate the burst-merging kernel from time series data, tested on synthetic models and applied to empirical examples.