Model manipulation and fitting library based on TensorFlow and optimised for simple and direct manipulation of probability density functions. Its main focus is on scalability, parallelisation and user friendly experience
Kernel Density Estimation in 1 dimension for large data sets. Overview (https://zfit.readthedocs.io/...
Major Features and Improvements reduce the memory footprint on (some) fits, especially repetitive (...
Major Features and Improvements Python 3.8 support Allow eager execution by setting with tf.config....
The zfit package is a model manipulation and fitting library based on TensorFlow and optimised for s...
zfit is a model fitting library completely implemented in Python and based on the Deep Learning fram...
Upgrade to TensorFlow 2.3 and support for weighted hessian error estimation. Added a one dimensional...
Statistical modeling is a key element in many scientific fields and especially in High-Energy Physic...
Add Kernel density estimation for 1 dimension. Major Features and Improvements add correlation meth...
0.4.0 (7.1.2020) This release switched to TensorFlow 2.0 eager mode. In case this breaks things for ...
Added many new minimizers from different libraries, all with uncertainty estimation available. All c...
Public release of binned fits and upgrade to Python 3.10 and TensorFlow 2.9. While binned fits are n...
With 0.3.2, bugfixes and three changes in the API/behavior Breaking changes tfp distributions wrapp...
0.4.1 (12.1.20) Release to keep up with TensorFlow 2.1 Major Features and Improvement
Major Features and Improvements add Python 3.9 support upgrade to TensorFlow 2.5 Bug fixes and sma...
zfit is a scalable, pythonic model fitting library that aims at implementing likelihood fits in HEP....
Kernel Density Estimation in 1 dimension for large data sets. Overview (https://zfit.readthedocs.io/...
Major Features and Improvements reduce the memory footprint on (some) fits, especially repetitive (...
Major Features and Improvements Python 3.8 support Allow eager execution by setting with tf.config....
The zfit package is a model manipulation and fitting library based on TensorFlow and optimised for s...
zfit is a model fitting library completely implemented in Python and based on the Deep Learning fram...
Upgrade to TensorFlow 2.3 and support for weighted hessian error estimation. Added a one dimensional...
Statistical modeling is a key element in many scientific fields and especially in High-Energy Physic...
Add Kernel density estimation for 1 dimension. Major Features and Improvements add correlation meth...
0.4.0 (7.1.2020) This release switched to TensorFlow 2.0 eager mode. In case this breaks things for ...
Added many new minimizers from different libraries, all with uncertainty estimation available. All c...
Public release of binned fits and upgrade to Python 3.10 and TensorFlow 2.9. While binned fits are n...
With 0.3.2, bugfixes and three changes in the API/behavior Breaking changes tfp distributions wrapp...
0.4.1 (12.1.20) Release to keep up with TensorFlow 2.1 Major Features and Improvement
Major Features and Improvements add Python 3.9 support upgrade to TensorFlow 2.5 Bug fixes and sma...
zfit is a scalable, pythonic model fitting library that aims at implementing likelihood fits in HEP....
Kernel Density Estimation in 1 dimension for large data sets. Overview (https://zfit.readthedocs.io/...
Major Features and Improvements reduce the memory footprint on (some) fits, especially repetitive (...
Major Features and Improvements Python 3.8 support Allow eager execution by setting with tf.config....
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