New features: Morphing-aware likelihood ratio estimators. See https://arxiv.org/abs/1805.00020 for a description. Implemented in madminer.ml.MorphingAwareRatioEstimator. Gradient clipping can now be set with the keyword clip_gradient in the estimator train() functions
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: Smarter sampling: MadMiner now keeps track of which events where generated (sampled) ...
New features: More observables for LHEReader.add_observable: Users can use "p_truth" to access part...
New features: New madminer.likelihood class will let the user define more powerful likelihood funct...
New features: New Fisher information geometry functionality in the madminer.fisherinformation.Infor...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: ParameterizedRatioEstimator now optionally rescales parameters (theta) to zero mean a...
New features: New keyword order in MadMiner.run(), which can be set to 'nlo' to set the systematics...
New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scal...
New features: Nuisance parameters for ratio-based methods! This required a major refactoring of mad...
New features: Clean separation between training and validation events: the SampleAugmenter function...
New features: Fisher information in histograms can be calculated for a custom binning MET noise in ...
New features: Expanded systematics system. Users now declare systematics with MadMiner.add_systemat...
New features: AsymptoticLimits functions are more memory efficient. New keyword histo_theta_batchsi...
New features: Dropout support Many more activation functions Number of workers for data loading can...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: Smarter sampling: MadMiner now keeps track of which events where generated (sampled) ...
New features: More observables for LHEReader.add_observable: Users can use "p_truth" to access part...
New features: New madminer.likelihood class will let the user define more powerful likelihood funct...
New features: New Fisher information geometry functionality in the madminer.fisherinformation.Infor...
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-...
New features: ParameterizedRatioEstimator now optionally rescales parameters (theta) to zero mean a...
New features: New keyword order in MadMiner.run(), which can be set to 'nlo' to set the systematics...
New features: Nuisance parameters to model systematic uncertainties, currently only from PDF / scal...
New features: Nuisance parameters for ratio-based methods! This required a major refactoring of mad...
New features: Clean separation between training and validation events: the SampleAugmenter function...
New features: Fisher information in histograms can be calculated for a custom binning MET noise in ...
New features: Expanded systematics system. Users now declare systematics with MadMiner.add_systemat...
New features: AsymptoticLimits functions are more memory efficient. New keyword histo_theta_batchsi...
New features: Dropout support Many more activation functions Number of workers for data loading can...
New features: Prototype implementation of joint score computations with finite differences (with Ma...
New features: Smarter sampling: MadMiner now keeps track of which events where generated (sampled) ...
New features: More observables for LHEReader.add_observable: Users can use "p_truth" to access part...
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