threeML.utils.statistics.likelihood_functions module
- 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)
- threeML.utils.statistics.likelihood_functions.poisson_observed_gaussian_background(observed_counts, background_counts, background_error, expected_model_counts)[source]
- threeML.utils.statistics.likelihood_functions.poisson_observed_poisson_background(observed_counts, background_counts, exposure_ratio, expected_model_counts)[source]
- 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)
- threeML.utils.statistics.likelihood_functions.regularized_log(vector)[source]
A function which is log(vector) where vector > 0, and zero otherwise.
- Parameters:
vector –
- Returns: