tfp.substrates.numpy.glm.compute_predicted_linear_response

Computes model_matrix @ model_coefficients + offset.

Main aliases

tfp.substrates.numpy.glm.compute_predicted_linear_response(
    model_matrix, model_coefficients, offset=None, name=None
)

Args
`model_matrix`	(Batch of) `float`-like, matrix-shaped `Tensor` where each row represents a sample's features.
`model_coefficients`	(Batch of) vector-shaped `Tensor` representing the model coefficients, one for each column in `model_matrix`. Must have same `dtype` as `model_matrix`.
`offset`	Optional `Tensor` representing constant shift applied to `predicted_linear_response`. Must broadcast to `response`. Default value: `None` (i.e., `tf.zeros_like(predicted_linear_response)`).
`name`	Python `str` used as name prefix to ops created by this function. Default value: `None` (i.e., `"compute_predicted_linear_response"`).

Returns
`predicted_linear_response`	`response`-shaped `Tensor` representing linear predictions based on new `model_coefficients`, i.e., `tf.linalg.matvec(model_matrix, model_coefficients) + offset`.

Args