tf.contrib.distributions.normal_conjugates_known_scale_posterior(
prior,
scale,
s,
n
)
Defined in tensorflow/contrib/distributions/python/ops/normal_conjugate_posteriors.py
.
Posterior Normal distribution with conjugate prior on the mean.
This model assumes that n
observations (with sum s
) come from a
Normal with unknown mean loc
(described by the Normal prior
)
and known variance scale**2
. The "known scale posterior" is
the distribution of the unknown loc
.
Accepts a prior Normal distribution object, having parameters
loc0
and scale0
, as well as known scale
values of the predictive
distribution(s) (also assumed Normal),
and statistical estimates s
(the sum(s) of the observations) and
n
(the number(s) of observations).
Returns a posterior (also Normal) distribution object, with parameters
(loc', scale'**2)
, where:
mu ~ N(mu', sigma'**2)
sigma'**2 = 1/(1/sigma0**2 + n/sigma**2),
mu' = (mu0/sigma0**2 + s/sigma**2) * sigma'**2.
Distribution parameters from prior
, as well as scale
, s
, and n
.
will broadcast in the case of multidimensional sets of parameters.
Args:
prior
:Normal
object of typedtype
: the prior distribution having parameters(loc0, scale0)
.scale
: tensor of typedtype
, taking valuesscale > 0
. The known stddev parameter(s).s
: Tensor of typedtype
. The sum(s) of observations.n
: Tensor of typeint
. The number(s) of observations.
Returns:
A new Normal posterior distribution object for the unknown observation
mean loc
.
Raises:
TypeError
: if dtype ofs
does not matchdtype
, orprior
is not a Normal object.