tfp.distributions.normal_conjugates_known_scale_posterior
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Posterior Normal distribution with conjugate prior on the mean.
tfp.distributions.normal_conjugates_known_scale_posterior(
prior, scale, s, n
)
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 type dtype:
the prior distribution having parameters (loc0, scale0).
|
scale
|
tensor of type dtype, taking values scale > 0.
The known stddev parameter(s).
|
s
|
Tensor of type dtype. The sum(s) of observations.
|
n
|
Tensor of type int. The number(s) of observations.
|
Returns |
|---|
A new Normal posterior distribution object for the unknown observation
mean loc.
|
Raises |
|---|
TypeError
|
if dtype of s does not match dtype, or prior is not a
Normal object.
|
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Last updated 2023-11-21 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21 UTC."],[],[],null,["# tfp.distributions.normal_conjugates_known_scale_posterior\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/distributions/normal_conjugate_posteriors.py#L149-L205) |\n\nPosterior Normal distribution with conjugate prior on the mean. \n\n tfp.distributions.normal_conjugates_known_scale_posterior(\n prior, scale, s, n\n )\n\nThis model assumes that `n` observations (with sum `s`) come from a\nNormal with unknown mean `loc` (described by the Normal `prior`)\nand known variance `scale**2`. The \"known scale posterior\" is\nthe distribution of the unknown `loc`.\n\nAccepts a prior Normal distribution object, having parameters\n`loc0` and `scale0`, as well as known `scale` values of the predictive\ndistribution(s) (also assumed Normal),\nand statistical estimates `s` (the sum(s) of the observations) and\n`n` (the number(s) of observations).\n\nReturns a posterior (also Normal) distribution object, with parameters\n`(loc', scale'**2)`, where: \n\n mu ~ N(mu', sigma'**2)\n sigma'**2 = 1/(1/sigma0**2 + n/sigma**2),\n mu' = (mu0/sigma0**2 + s/sigma**2) * sigma'**2.\n\nDistribution parameters from `prior`, as well as `scale`, `s`, and `n`.\nwill broadcast in the case of multidimensional sets of parameters.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|---------------------------------------------------------------------------------------------|\n| `prior` | `Normal` object of type `dtype`: the prior distribution having parameters `(loc0, scale0)`. |\n| `scale` | tensor of type `dtype`, taking values `scale \u003e 0`. The known stddev parameter(s). |\n| `s` | Tensor of type `dtype`. The sum(s) of observations. |\n| `n` | Tensor of type `int`. The number(s) of observations. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A new Normal posterior distribution object for the unknown observation mean `loc`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|-------------|----------------------------------------------------------------------------|\n| `TypeError` | if dtype of `s` does not match `dtype`, or `prior` is not a Normal object. |\n\n\u003cbr /\u003e"]]
View source on GitHub