LF1 Trials #
Category: 3-Local (LF1 repeated-trial probability space and i.i.d. preparation sampling).
This file introduces the repeated-preparation model used in LF1. Each trial begins from a fresh initial microstate sampled from the conditional preparation measure on the preparation region. The resulting product measure models repetition of preparation, not stochastic dynamics of a single trial.
Single-trial evolution remains deterministic at the ontic level.
A repeated-trial model for LF1.
Ω is the external sample space indexing repeated experimental runs.
P is the probability law on that sample space.
X n is the ontic initial microstate used on the n-th trial.
Each X n is measurable and has law equal to the preparation probability measure
S.prepMeasure. Independence and identical distribution are declared as explicit
hypotheses in Convergence.lean, where the law of large numbers is applied.
The probability law on the external sample space indexing repeated runs.
- X : ℕ → Ω → SigmaSpace
X nis the ontic initial microstate used on then-th trial. - hX_measurable (n : ℕ) : Measurable (self.X n)
Instances For
The event on the external sample space that the n-th trial lands in the
pulled-back outcome region associated with O.
Equations
- T.trialEvent O n = T.X n ⁻¹' O.preEvent
Instances For
The trial event is the preimage of the outcome region under the composition Φ ∘ X n.
This makes the deterministic structure explicit: a sample point ω is in the trial
event if and only if the n-th initial microstate X n ω, after evolving under the
ontic flow Φ, lands in the outcome region O.Ω.
The probability of the n-th trial landing in outcome region O, computed on
the external sample space.
Equations
- T.trialProb O n = ↑T.P (T.trialEvent O n)
Instances For
The trial probability agrees with the preparation weight of O.
All trial probabilities agree, because each trial has the same law.