threeML.utils.statistics.likelihood_functions module¶
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threeML.utils.statistics.likelihood_functions.
poisson_log_likelihood_ideal_bkg
(observed_counts, expected_bkg_counts, expected_model_counts)[source]¶ Poisson log-likelihood for the case where the background has no uncertainties:
L = sum_{i=0}^{N}~o_i~log{(m_i + b_i)} - (m_i + b_i) - log{o_i!}
- Parameters
observed_counts –
expected_bkg_counts –
expected_model_counts –
- Returns
(log_like vector, background vector)
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threeML.utils.statistics.likelihood_functions.
poisson_observed_gaussian_background
(observed_counts, background_counts, background_error, expected_model_counts)[source]¶
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threeML.utils.statistics.likelihood_functions.
poisson_observed_poisson_background
(observed_counts, background_counts, exposure_ratio, expected_model_counts)[source]¶
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threeML.utils.statistics.likelihood_functions.
poisson_observed_poisson_background_xs
(observed_counts, background_counts, exposure_ratio, expected_model_counts)[source]¶ Profile log-likelihood for the case when the observed counts are Poisson distributed, and the background counts are Poisson distributed as well (typical for X-ray analysis with aperture photometry). This has been derived by Keith Arnaud (see the Xspec manual, Wstat statistic)
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threeML.utils.statistics.likelihood_functions.
regularized_log
(vector)[source]¶ A function which is log(vector) where vector > 0, and zero otherwise.
- Parameters
vector –
- Returns