This provides an ipython notebook that implements maximum likelihood estimation (MLE) powerlaw fits for fast radio burst (FRB) fluence distributions, compares these to least-squared fits, and finds the MLE methods are significantly more robust, as detailed in Bilous et al. 2024 (arXiv:2407.05366).
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An ipython notebook that implements maximum likelihood estimation (MLE) powerlaw fits for fast radio burst (FRB) fluence distributions, compares these to least-squared fits, and finds the MLE methods are significantly more robust, as detailed in Bilous et al. 2024 (arxiv).
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TRASAL/FRB_powerlaw
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An ipython notebook that implements maximum likelihood estimation (MLE) powerlaw fits for fast radio burst (FRB) fluence distributions, compares these to least-squared fits, and finds the MLE methods are significantly more robust, as detailed in Bilous et al. 2024 (arxiv).
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