threeML.utils.statistics.stats_tools module
- class threeML.utils.statistics.stats_tools.PoissonResiduals(Non, Noff, alpha=1.0)[source]
Bases:
object
This class implements a way to compute residuals for a Poisson distribution mapping them to residuals of a standard normal distribution. The probability of obtaining the observed counts given the expected one is computed, and then transformed “in unit of sigma”, i.e., the sigma value corresponding to that probability is computed.
The algorithm implemented here uses different branches so that it is fairly accurate between -36 and +36 sigma.
NOTE: if the expected number of counts is not very high, then the Poisson distribution is skewed and so the probability of obtaining a downward fluctuation at a given sigma level is not the same as obtaining the same fluctuation in the upward direction. Therefore, the distribution of residuals is not expected to be symmetric in that case. The sigma level at which this effect is visible depends strongly on the expected number of counts. Under normal circumstances residuals are expected to be a few sigma at most, in which case the effect becomes important for expected number of counts <~ 15-20.
- class threeML.utils.statistics.stats_tools.Significance(Non, Noff, alpha=1)[source]
Bases:
object
Implements equations in Li&Ma 1983
- known_background()[source]
Compute the significance under the hypothesis that there is no uncertainty in the background. In other words, compute the probability of obtaining the observed counts given the expected counts from the background, then transform it in sigma.
NOTE: this is reliable for expected counts >~10-15 if the significance is not very high. The higher the expected counts, the more reliable the significance estimation. As rule of thumb, you need at least 25 counts to have reliable estimates up to 5 sigma.
NOTE 2: if you use to compute residuals in units of sigma, you should not expected them to be symmetrically distributed around 0 unless the expected number of counts is high enough for all bins (>~15). This is due to the fact that the Poisson distribution is very skewed at low counts.
- Returns
significance vector
- li_and_ma(assign_sign=True)[source]
Compute the significance using the formula from Li & Ma 1983, which is appropriate when both background and observed signal are counts coming from a Poisson distribution.
- Parameters
assign_sign – whether to assign a sign to the significance, according to the sign of the net counts
Non - alpha * Noff, so that excesses will have positive significances and defects negative significances :return:
- li_and_ma_equivalent_for_gaussian_background(sigma_b)[source]
Compute the significance using the formula from Vianello 2018 (https://iopscience.iop.org/article/10.3847/1538-4365/aab780/meta), which is appropriate when the observation is Poisson distributed but the background has been modeled and thus has Gaussian distributed errors.
- Parameters
sigma_b – The gaussian 1 sigma errors on the background
- Returns
- threeML.utils.statistics.stats_tools.aic(log_like, n_parameters, n_data_points)[source]
The Aikake information criterion. A model comparison tool based of infomormation theory. It assumes that N is large i.e., that the model is approaching the CLT.
- threeML.utils.statistics.stats_tools.bic(log_like, n_parameters, n_data_points)[source]
The Bayesian information criterion.
- threeML.utils.statistics.stats_tools.dic(bayes_analysis)[source]
elpd_DIC = log p(y|mean(parameters)) - p_DIC
the first term is the deviance at the mean of the posterior and p_DIC is the effective number of free parameters:
p_DIC = 2(log p(y|mean(parameters)) - 1/N sum(log p(y|parameters_s), 1,N) )
DIC = -2*elpd_DIC
the effective number of free parameters can be negative if the mean is the mean is far from the mode
- Parameters
bayes_analysis – a bayesian analysis object
- Return dic, effective number of free parameters