0.11.1 (20 Nov 2022)#

Hotfix for wrong import

0.11.0 (29 Nov 2022)#

Major Features and Improvements#

  • columns of unbinned data can be accessed with the obs like a mapping (like a dataframe)

  • speedup builtin errors method and make it more robust

Breaking changes#

  • Data can no longer be used directly as an array-like object but got mapping-like behavior.

  • some old deprecated methods were removed

Bug fixes and small changes#

  • improved caching speed, reduced tradeoff against memory

  • yields were not added correctly in some (especially binned) PDFs and the fit would fail

Requirement changes#

  • add jacobi (many thanks at @HansDembinski for the package)

0.10.1 (31 Aug 2022)#

Major Features and Improvements#

  • reduce the memory footprint on (some) fits, especially repetitive (loops) ones. Reduces the number of cached compiled functions. The cachesize can be set with and specifies the number of compiled functions that are kept in memory. The default is 10, but this can be tuned. Lower values can reduce memory usage, but potentially increase runtime.

Breaking changes#


Bug fixes and small changes#

  • Enable uniform binning for n-dimensional distributions with integer(s).

  • Sum of histograms failed for calling the pdf method (can be indirectly), integrated over wrong axis.

  • Binned PDFs expected binned spaces for limits, now unbinned limits are also allowed and automatically

    converted to binned limits using the PDFs binning.

  • Speedup sampling of binned distributions.

  • add to_binned and to_unbinned methods to PDF


Requirement changes#


  • Justin Skorupa for finding the bug in the sum of histograms and the missing automatic conversion of unbinned spaces to binned spaces.

0.10.0 (22. August 2022)#

Public release of binned fits and upgrade to Python 3.10 and TensorFlow 2.9.

Major Features and Improvements#

  • improved data handling in constructors from_pandas (which allows now to have weights as columns, dataframes that are a superset of the obs) and from_root (obs can now be spaces and therefore cuts can be direcly applied)

  • add hashing of unbinned datasets with a hashint attribute. None if no hash was possible.

Breaking changes#


Bug fixes and small changes#

  • SimpleLoss correctly supports both functions with implicit and explicit parameters, also if they are decorated.

  • extended sampling errored for some cases of binned PDFs.

  • ConstantParameter errored when converted to numpy.

  • Simultaneous binned fits could error with different binning due to a missing sum over a dimension.

  • improved stability in loss evaluation of constraints and poisson/chi2 loss.

  • reduce gradient evaluation time in errors for many parameters.

  • Speedup Parameter value assignement in fits, which is most notably when the parameter update time is comparably large to the fit evaluation time, such as is the case for binned fits with many nuisance parameters.

  • fix ipyopt was not pickleable in a fitresult

  • treat parameters sometimes as “stateless”, possibly reducing the number of retraces and reducing the memory footprint.


Requirement changes#

  • nlopt and ipyopt are now optional dependencies.

  • Python 3.10 added

  • TensorFlow >= 2.9.0, <2.11 is now required and the corresponding TensorFlow-Probability version >= 0.17.0, <0.19.0


  • @YaniBion for discovering the bug in the extended sampling and testing the alpha release

  • @ResStump for reporting the bug with the simultaneous binned fit


Major Features and Improvements#

  • Save results by pickling, unpickling a frozen (FitResult.freeze()) result and using zfit.param.set_values(params, result) to set the values of params.


  • the default name of the uncertainty methods hesse and errors depended on the method used (such as ‘minuit_hesse’, ‘zfit_errors’ etc.) and would be the exact method name. New names are now ‘hesse’ and ‘errors’, independent of the method used. This reflects better that the methods, while internally different, produce the same result. To update, use ‘hesse’ instead of ‘minuit_hesse’ or ‘hesse_np’ and ‘errors’ instead of ‘zfit_errors’ or ‘minuit_minos’ in order to access the uncertainties in the fitresult. Currently, the old names are still available for backwards compatibility. If a name was explicitly chosen in the error method, nothing changed.

Bug fixes and small changes#

  • KDE datasets are now correctly mirrored around observable space limits

  • multinomial sampling would return wrong results when invoked multiple times in graph mode due to a non-dynamic shape. This is fixed and the sampling is now working as expected.

  • increase precision in FitResult string representation and add that the value is rounded


  • schmitse for finding and fixing a mirroring bug in the KDEs

  • Sebastian Bysiak for finding a bug in the multinomial sampling


Major Features and Improvements#

  • Binned fits support, although limited in content, is here! This includes BinnedData, binned PDFs, and binned losses. TODO: extend to include changes/point to binned introduction.

  • new Poisson PDF

  • added Poisson constraint, LogNormal Constraint

  • Save results by pickling, unpickling a frozen (FitResult.freeze()) result and using zfit.param.set_values(params, result) to set the values of params.

Breaking changes#

  • params given in ComposedParameters are not sorted anymore. Rely on their name instead.

  • norm_range is now called norm and should be replaced everywhere if possible. This will break in the future.


Bug fixes and small changes#

  • remove warning when using rect_limits or similar.

  • gauss integral accepts now also tensor inputs in limits

  • parameters at limits is now shown correctly


Requirement changes#

  • add TensorFlow 2.7 support


0.8.3 (5 Apr 2022)#

  • fixate nlopt to < 2.7.1

0.8.2 (20 Sep 2021)#

Bug fixes and small changes#

  • fixed a longstanding bug in the DoubleCB implementation of the integral.

  • remove outdated deprecations

0.8.1 (14. Sep. 2021)#

Major Features and Improvements#

  • allow FitResult to freeze(), making it pickleable. The parameters are replaced by their name, the objects such as loss and minimizer as well.

  • improve the numerical integration by adding a one dimensional efficient integrator, testing for the accuracy of multidimensional integrals. If there is a sharp peak, this maybe fails to integrate and the number of points has to be manually raised

  • add highly performant kernel density estimation (mainly contributed by Marc Steiner) in 1 dimension which allow for the choice of arbitrary kernels, support boundary mirroring of the data and allow for large (millions) of data samples: - KDE1DimExact for the normal density estimation - KDE1DimGrid using a binning - KDE1DimFFT using a binning and FFT - KDE1DimISJ using a binning and an algorithm (ISJ) to solve the optimal bandwidth

    For an introduction, see either Kernel Density Estimation or the tutorial Model

  • add windows in CI

Breaking changes#

  • the numerical integration improved with more sensible values for tolerance. This means however that some fits will greatly increase the runtime. To restore the old behavior globally, do for each instance pdf.update_integration_options(draws_per_dim=40_000, max_draws=40_000, tol=1) This will integrate regardless of the chosen precision and it may be non-optimal. However, the precision estimate in the integrator is also not perfect and maybe overestimates the error, so that the integration by default takes longer than necessary. Feel free to play around with the parameters and report back.

Bug fixes and small changes#

  • Double crystallball: move a minus sign down, vectorize the integral, fix wrong output shape of pdf

  • add a minimal value in the loss to avoid NaNs when taking the log of 0

  • improve feedback when taking the derivative with respect to a parameter that a function does not depend on or if the function is purely Python.

  • make parameters deletable, especially it works now to create parameters in a function only and no NameAlreadyTakenError will be thrown.

Requirement changes#

  • add TensorFlow 2.6 support (now 2.5 and 2.6 are supported)


  • Marc Steiner for contributing many new KDE methods!

0.7.2 (7. July 2021)#

Bug fixes and small changes#

  • fix wrong arguments to minimize

  • make BaseMinimizer arguments optional

0.7.1 (6. July 2021)#

Bug fixes and small changes#

  • make loss callable with array arguments and therefore combatible with iminuit cost functions.

  • fix a bug that allowed FitResults to be valid that are actually invalid (reported by Maxime Schubiger).

0.7.0 (03 Jun 2021)#

Major Features and Improvements#

  • add Python 3.9 support

  • upgrade to TensorFlow 2.5

Bug fixes and small changes#

  • Scipy minimizers with hessian arguments use now BFGS as default

Requirement changes#

  • remove Python 3.6 support

  • boost-histogram

0.6.6 (12.05.2021)#

Update ipyopt requirement < 0.12 to allow numpy compatible with TensorFlow

0.6.5 (04.05.2021)#

  • hotfix for wrong argument in exponential PDF

  • removed requirement ipyopt, can be installed with pip install zfit[ipyopt] or by manually installing pip install ipyopt

0.6.4 (16.4.2021)#

Bug fixes and small changes#

  • remove requirement of Ipyopt on MacOS as no wheels are available. This rendered zfit basically non-installable.

0.6.3 (15.4.2021)#

Bug fixes and small changes#

  • fix loss failed for large datasets

  • catch hesse failing for iminuit


Minor small fixes.

Bug fixes and small changes#

  • add loss to callback signature that gives full access to the model

  • add create_new() to losses in order to re-instantiate them with new models and data preserving their current (and future) options and other arguments

0.6.1 (31.03.2021)#

Release for fix of minimizers that performed too bad

Breaking changes#

  • remove badly performing Scipy minimizers ScipyTrustKrylovV1 and ScipyTrustNCGV1

Bug fixes and small changes#

  • fix auto conversion to complex parameter using constructor

0.6.0 (30.3.2021)#

Added many new minimizers from different libraries, all with uncertainty estimation available.

Major Features and Improvements#

  • upgraded to TensorFlow 2.4

  • Added many new minimizers. A full list can be found in Minimize.

  • Completely new and overhauled minimizers design, including:

    • minimizers can now be used with arbitrary Python functions and an initial array independent of zfit

    • a minimization can be ‘continued’ by passing init to minimize

    • more streamlined arguments for minimizers, harmonized names and behavior.

    • Adding a flexible criterion (currently EDM) that will terminate the minimization.

    • Making the minimizer fully stateless.

    • Moving the loss evaluation and strategy into a LossEval that simplifies the handling of printing and NaNs.

    • Callbacks are added to the strategy.

  • Major overhaul of the FitResult, including:

    • improved zfit_error (equivalent of MINOS)

    • minuit_hesse and minuit_minos are now available with all minimizers as well thanks to an great improvement in iminuit.

    • Added an approx hesse that returns the approximate hessian (if available, otherwise empty)

  • upgrade to iminuit v2 changes the way it works and also the Minuit minimizer in zfit, including a new step size heuristic. Possible problems can be caused by iminuit itself, please report in case your fits don’t converge anymore.

  • improved compute_errors in speed by caching values and the reliability by making the solution unique.

  • increased stability for large datasets with a constant subtraction in the NLL

Breaking changes#

  • NLL (and extended) subtracts now by default a constant value. This can be changed with a new options argument. COMPARISON OF DIFFEREN NLLs (their absolute values) fails now! (flag can be deactivated)

  • BFGS (from TensorFlow Probability) has been removed as it is not working properly. There are many alternatives such as ScipyLBFGSV1 or NLoptLBFGSV1

  • Scipy (the minimizer) has been removed. Use specialized Scipy* minimizers instead.

  • Creating a zfit.Parameter, usign set_value or set_values now raises a ValueError if the value is outside the limits. Use assign to suppress it.


  • strategy to minimizer should now be a class, not an instance anymore.

Bug fixes and small changes#

  • zfit_error moved only one parameter to the correct initial position. Speedup and more reliable.

  • FFTconv was shifted if the kernel limits were not symetrical, now properly taken into account.

  • circumvent overflow error in sampling

  • shuffle samples from sum pdfs to ensure uniformity and remove conv sampling bias

  • create_sampler now samples immediately to allow for precompile, a new hook that will allow objects to optimize themselves.

Requirement changes#

  • ipyopt

  • nlopt

  • iminuit>=2.3

  • tensorflow ~= 2.4

  • tensorflow-probability~=12

For devs: - pre-commit - pyyaml - docformatter


  • Hans Dembinski for the help on upgrade to imituit V2

  • Thibaud Humair for helpful remarks on the parameters

0.5.6 (26.1.2020)#

Update to fix iminuit version

Bug fixes and small changes#

  • Fix issue when using a ComposedParameter as the rate argument of a Poisson PDF

Requirement changes#

  • require iminuit < 2 to avoid breaking changes

0.5.5 (20.10.2020)#

Upgrade to TensorFlow 2.3 and support for weighted hessian error estimation.

Added a one dimensional Convolution PDF

Major Features and Improvements#

  • upgrad to TensorFlow 2.3

Breaking changes#


Bug fixes and small changes#

  • print parameter inside function context works now correctly


  • Computation of the covariance matrix and hessian errors with weighted data

  • Convolution PDF (FFT in 1Dim) added (experimental, feedback welcome!)

Requirement changes#

  • TensorFlow==2.3 (before 2.2)

  • tensorflow_probability==0.11

  • tensorflow-addons # spline interpolation in convolution


0.5.4 (16.07.2020)#

Major Features and Improvements#

  • completely new doc design

Breaking changes#

  • Minuit uses its own, internal gradient by default. To change this back, use use_minuit_grad=False

  • minimize(params=...) now filters correctly non-floating parameters.

  • z.log has been moved to z.math.log (following TF)

Bug fixes and small changes#

  • ncalls is not correctly using the internal heuristc or the ncalls explicitly

  • minimize(params=...) automatically extracts independent parameters.

  • fix copy issue of KDEV1 and change name to ‘adaptive’ (instead of ‘adaptiveV1’)

  • change exp name of lambda_ to lam (in init)

  • add set_yield to BasePDF to allow setting the yield in place

  • Fix possible bug in SumPDF with extended pdfs (automatically)


Requirement changes#

  • upgrade to iminuit>=1.4

  • remove cloudpickle hack fix


Johannes for the docs re-design

0.5.3 (02.07.20)#

Kernel density estimation for 1 dimension.

Major Features and Improvements#

  • add correlation method to FitResult

  • Gaussian (Truncated) Kernel Density Estimation in one dimension zfit.pdf.GaussianKDE1DimV1 implementation with fixed and adaptive bandwidth added as V1. This is a feature that needs to be improved and feedback is welcome

  • Non-relativistic Breit-Wigner PDF, called Cauchy, implementation added.

Breaking changes#

  • change human-readable name of Gauss, Uniform and TruncatedGauss to remove the '_tfp' at the end of the name

Bug fixes and small changes#

  • fix color wrong in printout of results, params

  • packaging: moved to pyproject.toml and a setup.cfg mainly, development requirements can be installed with the dev extra as (e.g.) pip install zfit[dev]

  • Fix shape issue in TFP distributions for partial integration

  • change zfit internal algorithm (zfit_error) to compute error/intervals from the profile likelihood, which is 2-3 times faster than previous algorithm.

  • add from_minuit constructor to FitResult allowing to create it when using directly iminuit

  • fix possible bias with sampling using accept-reject

Requirement changes#

  • pin down cloudpickle version (upstream bug with pip install) and TF, TFP versions

0.5.2 (13.05.2020)#

Major Features and Improvements#

  • Python 3.8 and TF 2.2 support

  • easier debugigng with set_graph_mode that can also be used temporarily with a context manager. False will make everything execute Numpy-like.

Bug fixes and small changes#

  • added get_params to loss

  • fix a bug with the fixed_params when creating a sampler

  • improve exponential PDF stability and shift when normalized

  • improve accept reject sampling to account for low statistics

Requirement changes#

  • TensorFlow >= 2.2

0.5.1 (24.04.2020)#

(0.5.0 was skipped)

Complete refactoring of Spaces to allow arbitrary function. New, more consistent behavior with extended PDFs. SumPDF refactoring, more explicit handling of fracs and yields. Improved graph building allowing for more fine-grained control of tracing. Stabilized minimization including a push-back for NaNs.

Major Features and Improvements#

  • Arbitrary limits as well as vectorization (experimental) are now fully supported. The new Space has an additional argument for a function that tests if a vector x is inside.

    To test if a value is inside a space, Space.inside can be used. To filter values, Space.filter.

    The limits returned are now by default numpy arrays with the shape (1, n_obs). This corresponds well to the old layout and can, using z.unstack_x(lower) be treated like Data. This has also some consequences for the output format of rect_area: this is now a vector.

    Due to the ambiguity of the name limits, area etc (since they do only reflect the rectangular case) method with leading rect_* have been added (rect_limits, rect_area etc.) and are encouraged to be used.

  • Extending a PDF is more straightforward and removes any “magic”. The philosophy is: a PDF can be extended or not. But it does not change the fundamental behavior of functions.

  • SumPDF has been refactored and behaves now as follows: Giving in pdfs (extended or not or mixed) and fracs (either length pdfs or one less) will create a non-extended SumPDF using the fracs. The fact that the pdfs are maybe extended is ignored. This will lead to highly consistent behavior. If the number of fracs given equals the number of pdfs, it is up to the user (currently) to take care of the normalization. Only if all pdfs are extended and no fracs are given, the sumpdf will be using the yields as normalized fracs and be extended.

  • Improved graph building and z.function

    • the z.function can now, as with tf.function, be used either as a decorator without arguments or as a decorator with arguments. They are the same as in tf.function, except of a few additional ones.

    • allows to set the policy for whether everything is run in eager mode (graph=False), everything in graph, or most of it (graph=True) or an optimized variant, doing graph building only with losses but not just models (e.g. pdf won’t trigger a graph build, loss.value() will) with graph='auto'.

    • The graph cache can be cleaned manually using in order to prevent slowness in repeated tasks.

  • Switch for numerical gradients has been added as well in

  • Resetting to the default can be done with

  • Improved stability of minimizer by adding penalty (currently in Minuit) as default. To have a better behavior with toys (e.g. never fail on NaNs but return an invalid FitResult), use the DefaultToyStrategy in zfit.mnimize.

  • Exceptions are now publicly available in zfit.exception

  • Added nice printout for FitResult and FitResult.params.

  • get_params is now more meaningful, returning by default all independent parameters of the pdf, including yields. Arguments (floating, is_yield) allow for more fine-grained control.

Breaking changes#

  • Multiple limits are now handled by a MultiSpace class. Each Space has only “one limit” and no complicated layout has to be remembered. If you want to have a space that is defined in disconnected regions, use the + operator or functionally zfit.dimension.add_spaces

    To extract limits from multiple limits, MultiSpace and Space are both iterables, returning the containing spaces respectively itself (for the Space case).

  • SumPDF changed in the behavior. Read above in the Major Features and Improvement.

  • Integrals of extended PDFs are not extended anymore, but ext_integrate now returns the integral multiplied by the yield.


  • ComposedParameter takes now params instead of dependents as argument, it acts now as the arguments to the value_fn. To stay future compatible, create e.g. def value_fn(p1, pa2) and using params = ['param1, param2], value_fn will then be called as value_fn(param1, parma2). value_fn without arguments will probably break in the future.

  • FitResult.error has been renamed to errors to better reflect that multiple errors, the lower and upper are returned.

Bug fixes and small changes#

  • fix a (nasty, rounding) bug in sampling with multiple limits

  • fix bug in numerical calculation

  • fix bug in SimplePDF

  • fix wrong caching signature may lead to graph not being rebuild

  • add zfit.param.set_values method that allows to set the values of multiple parameters with one command. Can, as the set_value method be used with a context manager.

  • wrong size of weights when applying cuts in a dataset

  • with_coords did drop axes/obs

  • Fix function not traced when an error was raised during first trace

  • MultipleLimits support for analytic integrals

  • zfit.param.set_values(..) now also can use a FitResult as values argument to set the values from.


  • added a new error method, ‘zfit_error’ that is equivalent to ‘minuit_minos’, but not fully stable. It can be used with other minimizers as well, not only Minuit.

Requirement changes#

  • remove the outdated typing module

  • add tableformatter, colored, colorama for colored table printout


  • Johannes Lade for code review and discussions.

  • Hans Dembinski for useful inputs to the uncertainties.

0.4.3 (11.3.2020)#

Major Features and Improvements#

  • refactor hesse_np with covariance matrix, make it available to all minimizers

Behavioral changes#

Bug fixes and small changes#

  • fix bug in hesse_np

Requirement changes#


0.4.2 (27.2.2020)#

Major Features and Improvements#

  • Refactoring of the Constraints, dividing into ProbabilityConstraint that can be sampled from and more general constraints (e.g. for parameter boundaries) that can not be sampled from.

  • Doc improvements in the constraints.

  • Add hesse error method (‘hesse_np’) available to all minimizers (not just Minuit).

Behavioral changes#

  • Changed default step size to an adaptive scheme, a fraction (1e-4) of the range between the lower and upper limits.

Bug fixes and small changes#

  • Add use_minuit_grad option to Minuit optimizer to use the internal gradient, often for more stable fits

  • added experimental flag zfit.experimental_loss_penalty_nan, which adds a penalty to the loss in case the value is nan. Can help with the optimisation. Feedback welcome!

Requirement changes#


0.4.1 (12.1.20)#

Release to keep up with TensorFlow 2.1

Major Features and Improvements#

  • Fixed the comparison in caching the graph (implementation detail) that leads to an error.

0.4.0 (7.1.2020)#

This release switched to TensorFlow 2.0 eager mode. In case this breaks things for you and you need urgently a running version, install a version < 0.4.1. It is highly recommended to upgrade and make the small changes required.

Please read the upgrade guide <docs/project/upgrade_guide.rst> on a more detailed explanation how to upgrade.

TensorFlow 2.0 is eager executing and uses functions to abstract the performance critical parts away.

Major Features and Improvements#

  • Dependents (currently, and probably also in the future) need more manual tracking. This has mostly an effect on CompositeParameters and SimpleLoss, which now require to specify the dependents by giving the objects it depends (indirectly) on. For example, it is sufficient to give a ComplexParameter (which itself is not independent but has dependents) to a SimpleLoss as dependents (assuming the loss function depends on it).

  • ComposedParameter does no longer allow to give a Tensor but requires a function that, when evaluated, returns the value. It depends on the dependents that are now required.

  • Added numerical differentiation, which allows now to wrap any function with z.py_function (zfit.z). This can be switched on with zfit.settings.options['numerical_grad'] = True

  • Added gradient and hessian calculation options to the loss. Support numerical calculation as well.

  • Add caching system for graph to prevent recursive graph building

  • changed backend name to z and can be used as zfit.z or imported from it. Added:

    • function decorator that can be used to trace a function. Respects dependencies of inputs and automatically caches/invalidates the graph and recreates.

    • py_function, same as tf.py_function, but checks and may extends in the future

    • math module that contains autodiff and numerical differentiation methods, both working with tensors.

Behavioral changes#

  • EDM goal of the minuit minimizer has been reduced by a factor of 10 to 10E-3 in agreement with the goal in RooFits Minuit minimizer. This can be varied by specifying the tolerance.

  • known issue: the projection_pdf has troubles with the newest TF version and may not work properly (runs out of memory)

Bug fixes and small changes#

Requirement changes#

  • added numdifftools (for numerical differentiation)


0.3.7 (6.12.19)#

This is a legacy release to add some fixes, next release is TF 2 eager mode only release.

Major Features and Improvements#

  • mostly TF 2.0 compatibility in graph mode, tests against 1.x and 2.x

Behavioral changes#

Bug fixes and small changes#

  • get_depentents returns now an OrderedSet

  • errordef is now a (hidden) attribute and can be changed

  • fix bug in polynomials

Requirement changes#

  • added ordered-set

0.3.6 (12.10.19)#

Special release for conda deployment and version fix (TF 2.0 is out)

This is the last release before breaking changes occur

Major Features and Improvements#

  • added ConstantParameter and zfit.param namespace

  • Available on conda-forge

Behavioral changes#

  • an implicitly created parameter with a Python numerical (e.g. when instantiating a model) will be converted to a ConstantParameter instead of a fixed Parameter and therefore cannot be set to floating later on.

Bug fixes and small changes#

  • added native support TFP distributions for analytic sampling

  • fix Gaussian (TFP Distribution) Constraint with mixed up order of parameters

  • from_numpy automatically converts to default float regardless the original numpy dtype, dtype has to be used as an explicit argument

Requirement changes#

  • TensorFlow >= 1.14 is required


  • Chris Burr for the conda-forge deployment

0.3.4 (30-07-19)#

This is the last release before breaking changes occur

Major Features and Improvements#

  • create Constraint class which allows for more fine grained control and information on the applied constraints.

  • Added Polynomial models

  • Improved and fixed sampling (can still be slightly biased)

Behavioral changes#


Bug fixes and small changes#

  • fixed various small bugs


for the contribution of the Constraints to Matthieu Marinangeli <>

0.3.3 (15-05-19)#

Fixed Partial numeric integration

Bugfixes mostly, a few major fixes. Partial numeric integration works now.

  • data_range cuts are now applied correctly, also in several dimensions when a subset is selected (which happens internally of some Functors, e.g. ProductPDF). Before, only the selected obs was respected for cuts.

  • parital integration had a wrong take on checking limits (now uses supports).

0.3.2 (01-05-19)#

With 0.3.2, bugfixes and three changes in the API/behavior

Breaking changes#

  • tfp distributions wrapping is now different with dist_kwargs allowing for non-Parameter arguments (like other dists)

  • sampling allows now for importance sampling (sampler in Model specified differently)

  • model.sample now also returns a tensor, being consistent with pdf and integrate


  • shape handling of tfp dists was “wrong” (though not producing wrong results!), fixed. TFP distributions now get a tensor with shape (nevents, nobs) instead of a list of tensors with (nevents,)


  • refactor the sampling for more flexibility and performance (less graph constructed)

  • allow to use more sophisticated importance sampling (e.g. phasespace)

  • on-the-fly normalization (experimentally) implemented with correct gradient

0.3.1 (30-04-19)#

Minor improvements and bugfixes including:

  • improved importance sampling allowing to preinstantiate objects before it’s called inside the while loop

  • fixing a problem with ztf.sqrt

0.3.0 (2019-03-20)#

Beta stage and first pip release

0.0.1 (2018-03-22)#

  • First creation of the package.