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evidences will be set to 0. This produces a Bayes factor between the sampling power_prior and the hyperparameterised model. selection_function: func Function which evaluates your population selection function. conversion_function: func Function which converts a dictionary of sampled parameter to a dictionary of parameters of the population model. max_samples: int, optional Maximum number of samples to use from each set. cupy: bool If True and a compatible CUDA environment is available, cupy will be used for performance. Note: this requires setting up your hyper_prior properly. """ if cupy and not CUPY_LOADED: logger.warning("Cannot import cupy, falling back to numpy.") self.samples_per_posterior = max_samples self.data = self.resample_posteriors(posteriors, max_samples=max_samples) if not isinstance(hyper_prior, Model): hyper_prior = Model([hyper_prior]) self.hyper_prior = hyper_prior Likelihood.__init__(self, hyper_prior.parameters) if sampling_prior is not None: raise ValueError( "Passing a sampling_prior is deprecated and will be removed " "in the next release. This should be passed as a 'prior' " "column in the posteriors." ) elif "prior" in self.data: