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DEMetropolis can now tune both lambda and scaling parameters, but by default neither of them are tuned. See #3743 for more info.DEMetropolisZ, an improved variant of DEMetropolis brings better parallelization and higher efficiency with fewer chains with a slower initial convergence. This implementation is experimental. See #3784 for more info.DEMetropolis, DEMetropolisZ and the DifferentialEquation interface are now located in the Tutorials/Deep Dive section.fast_sample_posterior_predictive, a vectorized alternative to sample_posterior_predictive. This alternative is substantially faster for large models.sample_posterior_predictive can now feed on xarray.Dataset - e.g. from InferenceData.posterior. (see #3846)SamplerReport (MultiTrace.report) now has properties n_tune, n_draws, t_sampling for increased convenience (see #3827)pm.sample now has support for adapting dense mass matrix using QuadPotentialFullAdapt (see #3596, #3705, #3858, and #3893). Use init="adapt_full" or init="jitter+adapt_full" to use.Moyal distribution added (see #3870).pm.LKJCholeskyCov now automatically computes and returns the unpacked Cholesky decomposition, the correlations and the standard deviations of the covariance matrix (see #3881).Metropolis chains (see #3733 and #3796).pm.Data objects now get model-relative names (see #3843).pm.sample now takes 1000 draws and 1000 tuning samples by default, instead of 500 previously (see #3855).NegativeBinomial random method. Fixes #3864 in the style of #3509.MvNormal and MvStudentT (see 3836).arviz.InferenceData internally and avoids storing
pointwise log likelihood (see #3883).sample_ppc and sample_ppc_w that were deprecated in 3.6.sd in version 3.7 has been replaced by sigma now raises DeprecationWarning on using sd in continuous, mixed and timeseries distributions. (see #3837 and #3688).Text and SQLite backends and the save_trace/load_trace functions, since this is now done with ArviZ. (see #3902)pm.sample (see #3863).DifferentialEquation. See #3590 and #3634.Data and Deterministic variables when graphing models with graphviz. PR #3491.Matern12 covariance function for Gaussian processes. This is the Matern kernel with nu=1/2.@ operator now works with random variables and deterministics #3619.Rice, TruncatedNormal, Triangular and ZeroInflatedNegativeBinomial random methods. Math operations on values returned by draw_values might not broadcast well, and all the size aware broadcasting is left to generate_samples. Fixes #3481 and #3508
DEMetropolis) is now set via the cores argument. (#3559)Categorical.logp. In the case of multidimensional p's, the indexing was done wrong leading to incorrectly shaped tensors that consumed O(n**2) memory instead of O(n). This fixes issue #3535
OrderedLogistic.__init__ that unnecessarily increased the dimensionality of the underlying p. Related to issue issue #3535 but was not the true cause of it.pm.sample now it should be called using pm.sample_smc 3579
scaling and tune_scaling arguments as is a better idea to always allow SMC to automatically compute the scaling factor 3625
multiprocessong rather than psutil to count CPUs, which results in reliable core counts on Chromebooks.sample_posterior_predictive now preallocates the memory required for its output to improve memory usage. Addresses problems raised in this discourse thread.Categorical.logp. In the case of multidimensional p's, the indexing was done wrong leading to incorrectly shaped tensors that consumed O(n**2) memory instead of O(n). This fixes issue #3535
OrderedLogistic.__init__ that unnecessarily increased the dimensionality of the underlying p. Related to issue issue #3535 but was not the true cause of it.DensityDist.rand with generate_samples to make it aware of the distribution's shape. Added control flow attributes to still be able to behave as in earlier versions, and to control how to interpret the size parameter in the random callable signature. Fixes 3553
theano.gof.graph.Constant to type checks done in _draw_value (fixes issue 3595)HalfNormal did not used to work properly in draw_values, sample_prior_predictive, or sample_posterior_predictive (fixes issue 3686)pymc3.model.get_named_nodes_and_relations to use the ancestors and descendents, in a way that is consistent with theano's naming convention.pymc3.model.get_named_nodes_and_relations computes nodes without ancestors to make it robust to changes in var_name orderings (issue #3643)Data) that wraps the theano SharedVariable class and let the model be aware of its inputs and outputs.set_data to update variables defined as Data.Mixture now supports mixtures of multidimensional probability distributions, not just lists of 1D distributions.GLM.from_formula and LinearComponent.from_formula can extract variables from the calling scope. Customizable via the new eval_env argument. Fixing #3382.distributions.shape_utils module with functions used to help broadcast samples drawn from distributions using the size keyword argument.numpy.vectorize in distributions.distribution._compile_theano_function. This enables sample_prior_predictive and sample_posterior_predictive to ask for tuples of samples instead of just integers. This fixes issue #3422.sd as a parameter name have been renamed to sigma. sd will continue to function for backwards compatibility.HamiltonianMC was ignoring certain arguments like target_accept, and not using the custom step size jitter function with expectation 1.BrokenPipeError for parallel sampling more verbose on Windows.broadcast_distribution_samples function that helps broadcasting arrays of drawn samples, taking into account the requested size and the inferred distribution shape. This sometimes is needed by distributions that call several rvs separately within their random method, such as the ZeroInflatedPoisson (fixes issue #3310).Wald, Kumaraswamy, LogNormal, Pareto, Cauchy, HalfCauchy, Weibull and ExGaussian distributions random method used a hidden _random function that was written with scalars in mind. This could potentially lead to artificial correlations between random draws. Added shape guards and broadcasting of the distribution samples to prevent this (Similar to issue #3310).draw_values function was too permissive with what could be grabbed from inside point, which lead to an error when sampling posterior predictives of variables that depended on shared variables that had changed their shape after pm.sample() had been called (fix issue #3346).draw_values now adds the theano graph descendants of TensorConstant or SharedVariables to the named relationship nodes stack, only if these descendants are ObservedRV or MultiObservedRV instances (fixes issue #3354).MvNormal.random's usage of tensordot for Cholesky encoded covariances. This lead to wrong axis broadcasting and seemed to be the cause for issue #3343.Mixture.random when multidimensional mixtures were involved. The mixture component was not preserved across all the elements of the dimensions of the mixture. This meant that the correlations across elements within a given draw of the mixture were partly broken.Mixture.random to allow better use of vectorized calls to comp_dists.random.broadcast_distribution_samples in DiscreteWeibull.Mixture's default dtype is now determined by theano.config.floatX.dist_math.random_choice now handles nd-arrays of category probabilities, and also handles sizes that are not None. Also removed unused k kwarg from dist_math.random_choice.Categorical.mode to preserve all the dimensions of p except the last one, which encodes each category's probability.Categorical.p. p is now normalized to sum to 1 inside logp and random, but not during initialization. This could hide negative values supplied to p as mentioned in #2082.Categorical now accepts elements of p equal to 0. logp will return -inf if there are values that index to the zero probability categories.sigma, tau, and sd to signature of NormalMixture.pm.distributions.bound._ContinuousBounded and pm.distributions.bound._DiscreteBounded to use only and all positional arguments (fixes issue #3399).distributions.distribution.generate_samples to use the shape_utils module. This solves issues #3421 and #3147 by using the size aware broadcating functions in shape_utils.Multinomial.random and Multinomial.random_ methods to make them compatible with the new generate_samples function. In the process, a bug of the Multinomial.random_ shape handling was discovered and fixed.Bound.random where the point dictionary was passed to generate_samples as an arg instead of in not_broadcast_kwargs.Bound.random_ where total_size could end up as a float64 instead of being an integer if given size=tuple().model_graph that caused construction of the graph of the model for rendering to hang: replaced a search over the powerset of the nodes with a breadth-first search over the nodes. Fix for #3458.model_graph but left type hints (Fix for #3465). This means that we support python>=3.5.4.target_acceptfor HamiltonianMC is now 0.65, as suggested in Beskos et. al. 2010 and Neal 2001.draw_values that lead to intermittent errors in python3.5. This happened with some deterministic nodes that were drawn but not added to givens.nuts_kwargs and step_kwargs have been deprecated in favor of using the standard kwargs to pass optional step method arguments.SGFS and CSG have been removed (Fix for #3353). They have been moved to pymc3-experimental.live_plot and corresponding notebooks have been removed.approx_hessian was removed, due to numdifftools becoming incompatible with current scipy. The function was already optional, only available to a user who installed numdifftools separately, and not hit on any common codepaths. #3485.vars parameter of sample_posterior_predictive in favor of varnames.live_plot and corresponding notebooks have been removed.vars parameters of sample_posterior_predictive and sample_prior_predictive in favor of var_names. At least for the latter, this is more accurate, since the vars parameter actually took names.45 Luciano Paz
38 Thomas Wiecki
23 Colin Carroll
19 Junpeng Lao
15 Chris Fonnesbeck
13 Juan Martín Loyola
13 Ravin Kumar
8 Robert P. Goldman
5 Tim Blazina
4 chang111
4 adamboche
3 Eric Ma
3 Osvaldo Martin
3 Sanmitra Ghosh
3 Saurav Shekhar
3 chartl
3 fredcallaway
3 Demetri
2 Daisuke Kondo
2 David Brochart
2 George Ho
2 Vaibhav Sinha
1 rpgoldman
1 Adel Tomilova
1 Adriaan van der Graaf
1 Bas Nijholt
1 Benjamin Wild
1 Brigitta Sipocz
1 Daniel Emaasit
1 Hari
1 Jeroen
1 Joseph Willard
1 Juan Martin Loyola
1 Katrin Leinweber
1 Lisa Martin
1 M. Domenzain
1 Matt Pitkin
1 Peadar Coyle
1 Rupal Sharma
1 Tom Gilliss
1 changjiangeng
1 michaelosthege
1 monsta
1 579397
This will be the last release to support Python 2.
trace.get_sampler_stats('model_logp')) (#3134)incomplete_beta(a, b, value)
Beta, Cauchy, ExGaussian, Exponential, Flat, Gumbel, HalfCauchy, HalfFlat, HalfNormal, Laplace, Logistic, Lognormal, Normal, Pareto, StudentT, Triangular, Uniform, Wald, Weibull.sample_posterior_predictive is now to produce posterior predictive samples, in order, from all values of the trace. Previously, by default it would produce 1 chain worth of samples, using a random selection from the trace (#3212)distributions.distribution._DrawValuesContext context
manager. This is used to store the values already drawn in nested random
and draw_values calls, enabling draw_values to draw samples from the
joint probability distribution of RVs and not the marginals. Custom
distributions that must call draw_values several times in their random
method, or that invoke many calls to other distribution's random methods
(e.g. mixtures) must do all of these calls under the same _DrawValuesContext
context manager instance. If they do not, the conditional relations between
the distribution's parameters could be broken, and random could return
values drawn from an incorrect distribution.Rice distribution is now defined with either the noncentrality parameter or the shape parameter (#3287).c attribute handling in random and updated sample codes for consistency (#3225)ymin keyword in matplotlib's Axes.set_ylim (#3279)distribution.draw_values(params), will draw the params values from their joint probability distribution and not from combinations of their marginals (Refer to PR #3273).Multinomial._random method to better handle shape broadcasting (#3271)Rice distribution, which inconsistently mixed two parametrizations (#3286).Rice distribution now accepts multiple parameters and observations and is usable with NUTS (#3289).sample_posterior_predictive no longer calls draw_values to initialize the shape of the ppc trace. This called could lead to ValueError's when sampling the ppc from a model with Flat or HalfFlat prior distributions (Fix issue #3294).floatX and int32 for the continuous and discrete probability distribution parameters (addresses issue #3223).sample_ppc() and sample_ppc_w() to sample_posterior_predictive() and sample_posterior_predictive_w(), respectively.check_test_point method to pm.Model
Ordered Transformation and OrderedLogistic distributionChain transformationMass matrix contains zeros on the diagonal. Some derivatives might always be zero during tuning of pm.sample
NaN occurred in optimization. during ADVIpickle using pm.save_trace and pm.load_trace
Kumaraswamy distributionTruncatedNormal distributionsample_prior_predictive which allows for efficient sampling from the unconditioned model.sample, reduce autocorrelation from final trace.model_to_graphviz (which uses the optional dependency graphviz) to plot a directed graph of a PyMC3 model using plate notation.__dir__ to SingleGroupApproximation to improve autocompletion in interactive environmentsThere were 1 divergences ... could be raised.KeyError raised when only subset of variables are specified to be recorded in the trace.repeat=None arguments from all random() methods in distributions.sigma argument in MarginalSparse.marginal_likelihood in favor of noise
random. Now the random functionality is more robust and will work better for sample_prior when that is implemented.scale_cost_to_minibatch behaviour, previously this was not working and always False
logit_p keyword to pm.Bernoulli, so that users can specify the logit of the success probability. This is faster and more stable than using p=tt.nnet.sigmoid(logit_p).random keyword to pm.DensityDist thus enabling users to pass custom random method which in turn makes sampling from a DensityDist possible.pm.diagnostics.effective_n now can reports N_eff>N.KroneckerNormal distribution and a corresponding MarginalKron Gaussian Process implementation for efficient inference, along with lower-level functions such as cartesian and kronecker products.Coregion covariance function..glm.families.Binomial, with the flexibility of specifying the n.offset kwarg to .glm.compare function to accept a dictionary of model-trace pairs instead of two separate lists of models and traces.distribution.draw_values, now is also able to draw values from conditionally dependent RVs, such as autotransformed RVs (Refer to PR #2902).VonMises does not overflow for large values of kappa. i0 and i1 have been removed and we now use log_i0 to compute the logp.bw controlling the bandwidth.random method, while some intermediate node may have it. This meant that if the named node-input at the leaf of the graph did not have a fixed value, theano would try to compile it and fail to find inputs, raising a theano.gof.fg.MissingInputError. This was fixed by going through the theano variable's owner inputs graph, trying to get intermediate named-nodes values if the leafs had failed.distribution.draw_values, some named nodes could be theano.tensor.TensorConstants or theano.tensor.sharedvar.SharedVariables. Nevertheless, in distribution._draw_value, these would be passed to distribution._compile_theano_function as if they were theano.tensor.TensorVariables. This could lead to the following exceptions TypeError: ('Constants not allowed in param list', ...) or TypeError: Cannot use a shared variable (...). The fix was to not add theano.tensor.TensorConstant or theano.tensor.sharedvar.SharedVariable named nodes into the givens dict that could be used in distribution._compile_theano_function.njobs and nchains kwarg are deprecated in favor of cores and chains for sample
lag kwarg in pm.stats.autocorr and pm.stats.autocov is deprecated.advi+adapt_diag_grad and add jitter+adapt_diag_grad (#2643)MatrixNormal class for representing vectors of multivariate normal variablesHalfStudentT distributionDEMetropolis). For models with correlated dimensions that can not use gradient-based samplers, the DEMetropolis sampler can give higher effective sampling rates. (also see PR#2735)compareplot to use loo output.posteriorplot to scale fontssample_ppc_w now broadcastsdf_summary function renamed to summary
model.logp_array and model.bijection (#2724)sample_ppc and sample_ppc_w to iterate all chains(#2633, #2748)stats.r2_score (#2696) and test (#2729).minibatch-)advi is removed (#2781)This version includes two major contributions from our Google Summer of Code 2017 students:
OPVI) objects and Approximation objects. These make it easier to extend existing variational classes, and to derive inference from variational optimizations, respectively. The variational module now also includes normalizing flows (NFVI).gp) module. Standard GPs can be specified using either Latent or Marginal classes, depending on the nature of the underlying function. A Student-T process TP has been added. In order to accomodate larger datasets, approximate marginal Gaussian processes (MarginalSparse) have been added.Documentation has been improved as the result of the project's monthly "docathons".
An experimental stochastic gradient Fisher scoring (SGFS) sampling step method has been added.
The API for find_MAP was enhanced.
SMC now estimates the marginal likelihood.
Added Logistic and HalfFlat distributions to set of continuous distributions.
Bayesian fraction of missing information (bfmi) function added to stats.
Enhancements to compareplot added.
QuadPotential adaptation has been implemented.
Script added to build and deploy documentation.
MAP estimates now available for transformed and non-transformed variables.
The Constant variable class has been deprecated, and will be removed in 3.3.
DIC and BPIC calculations have been sped up.
Arrays are now accepted as arguments for the Bound class.
random method was added to the Wishart and LKJCorr distributions.
Progress bars have been added to LOO and WAIC calculations.
All example notebooks updated to reflect changes in API since 3.1.
Parts of the test suite have been refactored.
Fixed sampler stats error in NUTS for non-RAM backends
Matplotlib is no longer a hard dependency, making it easier to use in settings where installing Matplotlib is problematic. PyMC will only complain if plotting is attempted.
Several bugs in the Gaussian process covariance were fixed.
All chains are now used to calculate WAIC and LOO.
AR(1) log-likelihood function has been fixed.
Slice sampler fixed to sample from 1D conditionals.
Several docstring fixes.
The following people contributed to this release (ordered by number of commits):
Maxim Kochurov maxim.v.kochurov@gmail.com Bill Engels w.j.engels@gmail.com Chris Fonnesbeck chris.fonnesbeck@vanderbilt.edu Junpeng Lao junpeng.lao@unifr.ch Adrian Seyboldt adrian.seyboldt@gmail.com AustinRochford arochford@monetate.com Osvaldo Martin aloctavodia@gmail.com Colin Carroll colcarroll@gmail.com Hannes Vasyura-Bathke hannes.bathke@gmx.net Thomas Wiecki thomas.wiecki@gmail.com michaelosthege thecakedev@hotmail.com Marco De Nadai me@marcodena.it Kyle Beauchamp kyleabeauchamp@gmail.com Massimo mcavallaro@users.noreply.github.com ctm22396 ctm22396@gmail.com Max Horn maexlich@gmail.com Hennadii Madan madanh2014@gmail.com Hassan Naseri h.nasseri@gmail.com Peadar Coyle peadarcoyle@googlemail.com Saurav R. Tuladhar saurav@fastmail.com Shashank Shekhar shashank.f1@gmail.com Eric Ma ericmjl@users.noreply.github.com Ed Herbst ed.herbst@gmail.com tsdlovell dlovell@twosigma.com zaxtax zaxtax@users.noreply.github.com Dan Nichol daniel.nichol@univ.ox.ac.uk Benjamin Yetton bdyetton@gmail.com jackhansom jack.hansom@outlook.com Jack Tsai jacksctsai@gmail.com Andrés Asensio Ramos aasensioramos@gmail.com
New user forum at http://discourse.pymc.io
Much improved variational inference support:
Add Stein-Variational Gradient Descent as well as Amortized SVGD (experimental).
Added various optimizers including ADAM.
Stopping criterion implemented via callbacks.
sample() defaults changed: tuning is enabled for the first 500 samples which are then discarded from the trace as burn-in.
MvNormal supports Cholesky Decomposition now for increased speed and numerical stability.
Many optimizations and speed-ups.
NUTS implementation now matches current Stan implementation.
Add higher-order integrators for HMC.
ADVI stopping criterion implemented.
Improved support for theano's floatX setting to enable GPU computations (work in progress).
MvNormal supports Cholesky Decomposition now for increased speed and numerical stability.
Added support for multidimensional minibatches
Added Approximation class and the ability to convert a sampled trace into an approximation via its Empirical subclass.
Model can now be inherited from and act as a base class for user specified models (see pymc3.models.linear).
Add MvGaussianRandomWalk and MvStudentTRandomWalk distributions.
GLM models do not need a left-hand variable anymore.
Refactored HMC and NUTS for better readability.
Add support for Python 3.6.
Bound now works for discrete distributions as well.
Random sampling now returns the correct shape even for higher dimensional RVs.
Use theano Psi and GammaLn functions to enable GPU support for them.
We are proud and excited to release the first stable version of PyMC3, the product of more than 5 years of ongoing development and contributions from over 80 individuals. PyMC3 is a Python module for Bayesian modeling which focuses on modern Bayesian computational methods, primarily gradient-based (Hamiltonian) MCMC sampling and variational inference. Models are specified in Python, which allows for great flexibility. The main technological difference in PyMC3 relative to previous versions is the reliance on Theano for the computational backend, rather than on Fortran extensions.
Since the beta release last year, the following improvements have been implemented:
Added variational submodule, which features the automatic differentiation variational inference (ADVI) fitting method. Also supports mini-batch ADVI for large data sets. Much of this work was due to the efforts of Taku Yoshioka, and important guidance was provided by the Stan team (specifically Alp Kucukelbir and Daniel Lee).
Added model checking utility functions, including leave-one-out (LOO) cross-validation, BPIC, WAIC, and DIC.
Implemented posterior predictive sampling (sample_ppc).
Implemented auto-assignment of step methods by sample function.
Enhanced IPython Notebook examples, featuring more complete narratives accompanying code.
Extensive debugging of NUTS sampler.
Updated documentation to reflect changes in code since beta.
Refactored test suite for better efficiency.
Added von Mises, zero-inflated negative binomial, and Lewandowski, Kurowicka and Joe (LKJ) distributions.
Adopted joblib for managing parallel computation of chains.
Added contributor guidelines, contributor code of conduct and governance document.
Normal()Lognormal()HalfNormal()Old: Normal(name, mu, tau)
New: Normal(name, mu, sd) (supplying keyword arguments is unaffected).
MvNormal calling signature changed:
Old: MvNormal(name, mu, tau)
New: MvNormal(name, mu, cov) (supplying keyword arguments is unaffected).We on the PyMC3 core team would like to thank everyone for contributing and now feel that this is ready for the big time. We look forward to hearing about all the cool stuff you use PyMC3 for, and look forward to continued development on the package.
The following authors contributed to this release:
Chris Fonnesbeck chris.fonnesbeck@vanderbilt.edu John Salvatier jsalvatier@gmail.com Thomas Wiecki thomas.wiecki@gmail.com Colin Carroll colcarroll@gmail.com Maxim Kochurov maxim.v.kochurov@gmail.com Taku Yoshioka taku.yoshioka.4096@gmail.com Peadar Coyle (springcoil) peadarcoyle@googlemail.com Austin Rochford arochford@monetate.com Osvaldo Martin aloctavodia@gmail.com Shashank Shekhar shashank.f1@gmail.com
In addition, the following community members contributed to this release:
A Kuz for.akuz@gmail.com A. Flaxman abie@alum.mit.edu Abraham Flaxman abie@alum.mit.edu Alexey Goldin alexey.goldin@gmail.com Anand Patil anand.prabhakar.patil@gmail.com Andrea Zonca code@andreazonca.com Andreas Klostermann andreasklostermann@googlemail.com Andres Asensio Ramos Andrew Clegg andrew.clegg@pearson.com Anjum48 Benjamin Edwards bedwards@cs.unm.edu Boris Avdeev borisaqua@gmail.com Brian Naughton briannaughton@gmail.com Byron Smith Chad Heyne chadheyne@gmail.com Corey Farwell coreyf@rwell.org David Huard david.huard@gmail.com David Stück dstuck@users.noreply.github.com DeliciousHair mshepit@gmail.com Dustin Tran Eigenblutwurst Hannes.Bathke@gmx.net Gideon Wulfsohn gideon.wulfsohn@gmail.com Gil Raphaelli g@raphaelli.com Gogs gogitservice@gmail.com Ilan Man Imri Sofer imrisofer@gmail.com Jake Biesinger jake.biesinger@gmail.com James Webber jamestwebber@gmail.com John McDonnell john.v.mcdonnell@gmail.com Jon Sedar jon.sedar@applied.ai Jordi Diaz Jordi Warmenhoven jordi.warmenhoven@gmail.com Karlson Pfannschmidt kiudee@mail.uni-paderborn.de Kyle Bishop citizenphnix@gmail.com Kyle Meyer kyle@kyleam.com Lin Xiao Mack Sweeney mackenzie.sweeney@gmail.com Matthew Emmett memmett@unc.edu Michael Gallaspy gallaspy.michael@gmail.com Nick nalourie@example.com Osvaldo Martin aloctavodia@gmail.com Patricio Benavente patbenavente@gmail.com Raymond Roberts Rodrigo Benenson rodrigo.benenson@gmail.com Sergei Lebedev superbobry@gmail.com Skipper Seabold chris.fonnesbeck@vanderbilt.edu Thomas Kluyver takowl@gmail.com Tobias Knuth mail@tobiasknuth.de Volodymyr Kazantsev Wes McKinney wesmckinn@gmail.com Zach Ploskey zploskey@gmail.com akuz for.akuz@gmail.com brandon willard brandonwillard@gmail.com dstuck dstuck88@gmail.com ingmarschuster ingmar.schuster.linguistics@gmail.com jan-matthis mail@jan-matthis.de jason JasonTam22@gmailcom kiudee quietdeath@gmail.com maahnman github@mm.maahn.de macgyver neil.rabinowitz@merton.ox.ac.uk mwibrow mwibrow@gmail.com olafSmits o.smits@gmail.com paul sorenson paul@metrak.com redst4r redst4r@web.de santon steven.anton@idanalytics.com sgenoud stevegenoud+github@gmail.com stonebig Tal Yarkoni tyarkoni@gmail.com x2apps x2apps@yahoo.com zenourn daniel@zeno.co.nz
Probabilistic programming allows for flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source probabilistic programmer framework with an intuitive, readable and concise, yet powerful, syntax that is close to the natural notation statisticians use to describe models. It features next-generation fitting techniques, such as the No U-Turn Sampler, that allow fitting complex models with thousands of parameters without specialized knowledge of fitting algorithms.
PyMC3 has recently seen rapid development. With the addition of two new major features: automatic transforms and missing value imputation, PyMC3 has become ready for wider use. PyMC3 is now refined enough that adding features is easy, so we don't expect adding features in the future will require drastic changes. It has also become user friendly enough for a broader audience. Automatic transformations mean NUTS and find_MAP work with less effort, and friendly error messages mean its easy to diagnose problems with your model.
Thus, Thomas, Chris and I are pleased to announce that PyMC3 is now in Beta.
transform= argument on Distributions. model.TransformedVar is gone.model.profile(model.logpt)
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