response-shaped Tensor representing linear
predictions based on new model_coefficients, i.e.,
tfp.glm.compute_predicted_linear_response(
model_matrix, model_coefficients, offset).
name
Python str used as TF namescope for ops created by member
functions. Default value: None (i.e., 'log_prob').
Computes mean(r), var(mean), d/dr mean(r) for linear response, r.
Here mean and var are the mean and variance of the sufficient statistic,
which may not be the same as the mean and variance of the random variable
itself. If the distribution's density has the form
p_Y(y) = h(y) Exp[dot(theta, T(y)) - A]
where theta and A are constants and h and T are known functions,
then mean and var are the mean and variance of T(Y). In practice,
often T(Y) := Y and in that case the distinction doesn't matter.
Python str used as TF namescope for ops created by member
functions. Default value: None (i.e., 'call').
Returns
mean
Tensor with shape and dtype of predicted_linear_response
representing the distribution prescribed mean, given the prescribed
linear-response to mean mapping.
variance
Tensor with shape and dtype of predicted_linear_response
representing the distribution prescribed variance, given the prescribed
linear-response to mean mapping.
grad_mean
Tensor with shape and dtype of predicted_linear_response
representing the gradient of the mean with respect to the
linear-response and given the prescribed linear-response to mean
mapping.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21 UTC."],[],[]]