The other fitters, which use the curvature at the peak to estimate The other distributions, the estimated entropy is within uncertainty ofĪctual value, but the uncertainty is a bit high. Ideally, the entropy estimated by bumps will match the predicted entropy #Entropy calculator pdf#T 84 # close to normal t 4 # high kurtosis uniform - 5 100 # high entropy cauchy 0 1 # undefined variance expon 0.1 0.2 # asymmetric, narrow beta 0.5 0.5 # 'antimodal' u-shaped pdf beta 2 5 # skewed mvn 1, 1, 1 1, 2, 3 # 3-D multivariate standard normal at (1,2,3) mvt 4 1, 1, 1, 1, 1 # 5-D multivariate t-distribution with df=4 at origin mvu 1, 1, 1, 1, 1 # 5-D unit uniform distribution centered at origin mvcauchy 1, 1, 1 # 3-D multivariate Cauchy distribution at origin mvskewn 5 1, 1, 1 # 3-D multivariate skew normal with alpha=5 at origin This model file will not work for multivariate Only argument to this function is the parameter value x, which becomes theįitting parameter. Is not being used to fit data, but instead to explore the probabilityĭistribution directly through the negative log likelihood function. Set the fitting problem using the direct PDF method. argv ] D = D_class ( * args ) else : print ( USAGE ) sys. split ( ',' )] if ' ' in v else if ',' in v else float ( v ) for v in sys. get ( dist_name, None ) if D_class is None : D_class = getattr ( distributions, dist_name, None ) if D_class is None : print ( "unknown distribution " + dist_name ) sys. shape ) Dk = return Joint ( Dk ) def mvt ( df, sigma, mu = None ): mu, sigma = _mu_sigma ( mu, sigma ) return MultivariateT ( mu = mu, sigma = sigma, df = df ) def mvcauchy ( sigma, mu = None ): mu, sigma = _mu_sigma ( mu, sigma ) return MultivariateT ( mu = mu, sigma = sigma, df = 1 ) DISTS = if len ( sys. shape ) return mu, sigma def mvn ( sigma, mu = None ): mu, sigma = _mu_sigma ( mu, sigma ) return multivariate_normal ( mean = mu, cov = sigma ) def mvskewn ( alpha, sigma, mu = None ): sigma = np. diag ( sigma ** 2 ) if mu is None : mu = np. For example, for the normal distribution, x ~ N(3, 0.8), use: bumps -fit=dream -entropy -store=/tmp/T1 check_entropy.py norm 3 0.2 """ def _mu_sigma ( mu, sigma ): sigma = np. are the arguments for the distribution in the order that they appear. where dist is one of the distributions in and p1, p2. USAGE = """ Usage: bumps check_entropy.py dist p1 p2.
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