Skip to content
A user-centered Python package for differentiable probabilistic inference
Python
Branch: master
Clone or download
LucaAmbrogioni Merge pull request #19 from SvenDH/master
documented all standard variables
Latest commit 5c58951 Aug 4, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.eggs Initial commit Jun 15, 2019
brancher documented all standard variables Aug 4, 2019
development_playgrounds fix on biased mode of WCGD Jul 30, 2019
examples
tests
.env Initial commit Jun 15, 2019
.gitignore Work on convolution functions, bug fix Jun 19, 2019
LICENSE Initial commit Jun 15, 2019
README.md Fix typo Jun 22, 2019
manage.py Initial commit Jun 15, 2019
requirements.txt Initial commit Jun 15, 2019
setup.cfg Initial commit Jun 15, 2019
setup.py Add __version__ in one place Jul 22, 2019

README.md

Brancher: A user-centered Python package for differentiable probabilistic inference

Brancher allows to design and train differentiable Bayesian models using stochastic variational inference. Brancher is based on the deep learning framework PyTorch.

Building probabilistic models

Probabilistic models are defined symbolically. Random variables can be created as follows:

a = NormalVariable(loc = 0., scale = 1., name = 'a')
b = NormalVariable(loc = 0., scale = 1., name = 'b')

It is possible to chain together random variables by using arithmetic and mathematical functions:

c = NormalVariable(loc = a**2 + BF.sin(b), 
                   scale = BF.exp(b), 
                   name = 'a')

In this way, it is possible to create arbitrarely complex probabilistic models. It is also possible to use all the deep learning tools of PyTorch in order to define probabilistic models with deep neural networks.

Example: Autoregressive modeling

Probabilistic model

Probabilistic models are defined symbolically:

T = 20
driving_noise = 1.
measure_noise = 0.3
x0 = NormalVariable(0., driving_noise, 'x0')
y0 = NormalVariable(x0, measure_noise, 'x0')
b = LogitNormalVariable(0.5, 1., 'b')

x = [x0]
y = [y0]
x_names = ["x0"]
y_names = ["y0"]
for t in range(1,T):
    x_names.append("x{}".format(t))
    y_names.append("y{}".format(t))
    x.append(NormalVariable(b*x[t-1], driving_noise, x_names[t]))
    y.append(NormalVariable(x[t], measure_noise, y_names[t]))
AR_model = ProbabilisticModel(x + y)

Observe data

Once the probabilistic model is define, we can decide which variable is observed:

[yt.observe(data[yt][:, 0, :]) for yt in y]

Autoregressive variational distribution

The variational distribution can have an arbitrary structure:

Qb = LogitNormalVariable(0.5, 0.5, "b", learnable=True)
logit_b_post = DeterministicVariable(0., 'logit_b_post', learnable=True)
Qx = [NormalVariable(0., 1., 'x0', learnable=True)]
Qx_mean = [DeterministicVariable(0., 'x0_mean', learnable=True)]
for t in range(1, T):
    Qx_mean.append(DeterministicVariable(0., x_names[t] + "_mean", learnable=True))
    Qx.append(NormalVariable(BF.sigmoid(logit_b_post)*Qx[t-1] + Qx_mean[t], 1., x_names[t], learnable=True))
variational_posterior = ProbabilisticModel([Qb] + Qx)
model.set_posterior_model(variational_posterior)

Inference

Now that the models are specified we can perform approximate inference using stochastic gradient descent:

inference.perform_inference(AR_model, 
                            number_iterations=500,
                            number_samples=300,
                            optimizer="SGD",
                            lr=0.001)
You can’t perform that action at this time.