View source on GitHub 
Oneparameter logistic itemresponse theory (IRT) model.
Inherits From: ItemResponseTheory
tfp.experimental.substrates.numpy.experimental.inference_gym.targets.SyntheticItemResponseTheory()
This uses a dataset sampled from the prior. This dataset is a simulation of 400 students each answering a subset of 100 unique questions, with a total of 30012 questions answered.
Args  

train_student_ids

Integer Tensor with shape [num_train_points] .
training student ids, ranging from 0 to num_students .

train_question_ids

Integer Tensor with shape [num_train_points] .
training question ids, ranging from 0 to num_questions .

train_correct

Integer Tensor with shape [num_train_points] . Whether
the student in the training set answered the question correctly, either
0 or 1.

test_student_ids

Integer Tensor with shape [num_test_points] . Testing
student ids, ranging from 0 to num_students . Can be None , in which
case testrelated sample transformations are not computed.

test_question_ids

Integer Tensor with shape [num_test_points] .
Testing question ids, ranging from 0 to num_questions . Can be None ,
in which case testrelated sample transformations are not computed.

test_correct

Integer Tensor with shape [num_test_points] . Whether the
student in the testing set answered the question correctly, either 0 or

name

Python str name prefixed to Ops created by this class.

pretty_name

A Python str . The pretty name of this model.

Raises  

ValueError

If test_student_ids , test_question_ids or test_correct
are not either all None or are all specified.

ValueError

If the parallel arrays are not all of the same size. 
Attributes  

default_event_space_bijector

Bijector mapping the reals (R**n) to the event space of this model. 
dtype

The DType of Tensor s handled by this model.

event_shape

Shape of a single sample from as a TensorShape .
May be partially defined or unknown. 
name

Python str name prefixed to Ops created by this class.

sample_transformations

A dictionary of names to SampleTransformation s.

Child Classes
Methods
log_likelihood
log_likelihood(
value, name='log_likelihood'
)
Evaluates the log_likelihood at value
.
prior_distribution
prior_distribution(
name='prior_distribution'
)
The prior distribution over the model parameters.
unnormalized_log_prob
unnormalized_log_prob(
value, name='unnormalized_log_prob'
)
The unnormalized log density of evaluated at a point.
This corresponds to the target distribution associated with the model, often its posterior.
Args  

value

A (nest of) Tensor to evaluate the log density at.

name

Python str name prefixed to Ops created by this method.

Returns  

unnormalized_log_prob

A floating point Tensor .
