TensorFlow Probability distributions

Every distribution in TFP has a shape, batch, and event size associated with it. The shape is the sample size; it represents independent and identically distributed draws or observations. Consider the normal distribution that we defined in the previous section:

normal = tfd.Normal(loc=0., scale=1.)

This defines a single normal distribution, with mean zero and standard deviation one. When we use the sample function, we do a random draw from this distribution.

Notice the details regarding batch_shape and event_shape if you print the object normal:

print(normal)

>>> tfp.distributions.Normal("Normal", batch_shape=[], event_shape=[], dtype=float32)

Let us try and define a second normal object, but this time, loc and scale are lists:

normal_2 = tfd.Normal(loc=[0., 0.], scale=[1., 3.])
print(normal_2)

>>> tfp.distributions.Normal("Normal", batch_shape=[2], event_shape=[], dtype...