function certify!(fb::FullBunching)

Returns the p_value of the null and alternative hypothesis.

function certify!(fb::FullBunching)

Returns the p_value of the null and alternative hypothesis.

certify!(b::Bayesian)

Updates all probabilities associated with a Bayesian Certifier.

compute_confidence(events,p_q, p_a)

A bayesian confidence estimator: return the probability that the null hypothesis Q is right compared to the alternative hypothesis A.

compute_confidence_array(events, p_q, p_a)

Return an array of the probabilities of H being true as we process more and more events.

number_of_samples(evb::Event{TIn, TOut}, evd::Event{TIn, TOut}; p_null = 0.95, maxiter = 10000) where {TIn <:InputType, TOut <:PartitionCountsAll}

Outputs the number of samples required to attain a confidence that the null hypothesis (underlied by the parameters sent in evb) is true compared the alternative (underlied by evd) through a bayesian partition sample.

Note that this gives a specific sample - this function should be averaged over many trials to obtain a reliable estimate.

p_B(event::Event{TIn, TOut}) where {TIn<:InputType, TOut <: FockDetection}

Outputs the probability that a given FockDetection would have if the InputType was Bosonic for this event.

p_D(event::Event{TIn, TOut}) where {TIn<:InputType, TOut <: FockDetection}

Outputs the probability that a given FockDetection would have if the InputType was Distinguishable for this event.

p_x(event::Event{TIn, TOut},x) where {TIn<:InputType, TOut <: FockDetection}

Outputs the probability that a given FockDetection would have if the InputType was OneParameterInterpolation with distinguishability x for this event.

p_x_imperfect_source(params_event::SamplingParameters ,x, source::QuantumDot)

Outputs the probability that a given FockDetection would have if the InputType was OneParameterInterpolation with distinguishability x for this event. This averages over all possible inputs compatible with the number of lost photons

p_x_imperfect_source_update_this_event(event::Event{TIn, TOut},params_event::SamplingParameters, source::QuantumDot) where {TIn<:InputType, TOut <: Union{FockDetection, ThresholdFockDetection}}

Same as above but eating an Event so as to keep the previous workflow working.

Still need to create at once the params_event at the beginnig of the validation process.