Derived Models¶
Introduction¶
QInfer provides several models which decorate other models, providing additional functionality or changing the behaviors of underlying models.
PoisonedModel
 Model corrupted by likelihood errors¶

class
qinfer.derived_models.
PoisonedModel
(underlying_model, tol=None, n_samples=None, hedge=None)[source]¶ Bases:
qinfer.derived_models.DerivedModel
Model that simulates sampling error incurred by the MLE or ALE methods of reconstructing likelihoods from sample data. The true likelihood given by an underlying model is perturbed by a normally distributed random variable \(\epsilon\), and then truncated to the interval \([0, 1]\).
The variance of \(\epsilon\) can be specified either as a constant, to simulate ALE (in which samples are collected until a given threshold is met), or as proportional to the variance of a possiblyhedged binomial estimator, to simulate MLE.
Parameters: 
simulate_experiment
(modelparams, expparams, repeat=1)[source]¶ Simulates experimental data according to the original (unpoisoned) model. Note that this explicitly causes the simulated data and the likelihood function to disagree. This is, strictly speaking, a violation of the assumptions made about
Model
subclasses. This violation is by intention, and allows for testing the robustness of inference algorithms against errors in that assumption.

BinomialModel
 Model over batches of twooutcome experiments¶

class
qinfer.derived_models.
BinomialModel
(underlying_model)[source]¶ Bases:
qinfer.derived_models.DerivedModel
Model representing finite numbers of iid samples from another model, using the binomial distribution to calculate the new likelihood function.
Parameters: underlying_model (qinfer.abstract_model.Model) – An instance of a two outcome model to be decorated by the binomial distribution. Note that a new experimental parameter field
n_meas
is added by this model. This parameter field represents how many times a measurement should be made at a given set of experimental parameters. To ensure the correct operation of this model, it is important that the decorated model does not also admit a field with the namen_meas
.
decorated_model
¶

expparams_dtype
¶

is_n_outcomes_constant
¶ Returns
True
if and only if the number of outcomes for each experiment is independent of the experiment being performed.This property is assumed by inference engines to be constant for the lifetime of a Model instance.

n_outcomes
(expparams)[source]¶ Returns an array of dtype
uint
describing the number of outcomes for each experiment specified byexpparams
.Parameters: expparams (numpy.ndarray) – Array of experimental parameters. This array must be of dtype agreeing with the expparams_dtype
property.
