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.. currentmodule:: qinfer

Representing Probability Distributions


Probability distributions such as prior distributions over model parameters are reprented in QInfer by objects of type :class:`Distribution` that are responsible for producing samples according to those distributions. This is especially useful, for instance, when drawing initial particles for use with an :class:`~qinfer.smc.SMCUpdater`.

The approach to representing distributions taken by QInfer is somewhat different to that taken by, for example, :mod:`scipy.stats`, in that a QInfer :class:`Distribution` is a class that produces samples according to that distribution. This means that QInfer :class:`Distribution` objects provide much less information than do those represented by objects in :mod:`scipy.stats`, but that they are much easier to write and combine.

Sampling Pre-made Distributions

QInfer comes along with several distributions, listed in :ref:`specific_distributions`. Each of these is a subclass of :class:`Distribution`, and hence has a method :meth:`~Distribution.sample` that produces an array of samples.

>>> from qinfer import NormalDistribution
>>> dist = NormalDistribution(0, 1)
>>> samples = dist.sample(n=5)
>>> samples.shape == (5, 1)

Combining Distributions

Distribution objects can be combined using other distribution objects. For instance, if a \sim \mathcal{N}(0, 1) and b \sim \text{Uni}(0, 1), then the product distribution on (a,b) can be produced by using :class:`ProductDistribution`:

>>> from qinfer import UniformDistribution, ProductDistribution
>>> a = NormalDistribution(0, 1)
>>> b = UniformDistribution([0, 1])
>>> ab = ProductDistribution(a, b)
>>> samples = ab.sample(n=5)
>>> samples.shape == (5, 2)

Making Custom Distributions

To make a custom distribution, one need only implement :meth:`~Distribution.sample` and set the property :attr:`~Distribution.n_rvs` to indicate how many random variables the new distribution class represents.

For example, to implement a distribution over x and y such that \sqrt{x^2 + y^2} \sim \mathcal{N}(1, 0.1) and such that the angle between x and y is drawn from \text{Uni}(0, 2 \pi):

from qinfer import Distribution

class RingDistribution(Distribution):
    def n_rvs(self):
        return 2

    def sample(self, n=1):
        r = np.random.randn(n, 1) * 0.1 + 1
        th = np.random.random((n, 1)) * 2 * np.pi

        x = r * np.cos(th)
        y = r * np.sin(th)

        return np.concatenate([x, y], axis=1)