Revision 31dbbe5fe591a744bf8481f0d07773c1f42159d1 authored by joelberkeley-pio on 11 June 2020, 14:37:47 UTC, committed by GitHub on 11 June 2020, 14:37:47 UTC
`Release 2.0.5`
2 parent s 435a7ff + 3669d13
probability_distributions.py
``````# Copyright 2017 the GPflow authors.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

# Eventually, it would be nice to not have to have our own classes for
# proability distributions. The TensorFlow "distributions" framework would
# be a good replacement.
from .base import TensorType

class ProbabilityDistribution:
"""
This is the base class for a probability distributions,
over which we take the expectations in the expectations framework.
"""

class Gaussian(ProbabilityDistribution):
def __init__(self, mu: TensorType, cov: TensorType):
self.mu = mu  # [N, D]
self.cov = cov  # [N, D, D]

class DiagonalGaussian(ProbabilityDistribution):
def __init__(self, mu: TensorType, cov: TensorType):
self.mu = mu  # [N, D]
self.cov = cov  # [N, D]

class MarkovGaussian(ProbabilityDistribution):
"""
Gaussian distribution with Markov structure.
Only covariances and covariances between t and t+1 need to be
parameterised. We use the solution proposed by Carl Rasmussen, i.e. to
represent
Var[x_t] = cov[x_t, :, :] * cov[x_t, :, :].T
Cov[x_t, x_{t+1}] = cov[t, :, :] * cov[t+1, :, :]
"""

def __init__(self, mu: TensorType, cov: TensorType):
self.mu = mu  # N+[1, D]
self.cov = cov  # 2 x (N+1)[, D, D]
``````

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