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import Mathlib.Probability.Notation
import GlimpseOfLean.Library.Probability
set_option linter.unusedSectionVars false
set_option autoImplicit false
set_option linter.unusedTactic false
set_option linter.unusedVariables false
noncomputable section
open scoped ENNReal

Probability measures, independent sets

We introduce a probability space and events (measurable sets) on that space. We then define independence of events and conditional probability, and prove results relating those two notions.

Mathlib has a (different) definition for independence of sets and also has a conditional measure given a set. Here we practice instead on simple new definitions to apply the tactics introduced in the previous files.

We open namespaces. The effect is that after that command, we can call lemmas in those namespaces without their namespace prefix: for example, we can write inter_comm instead of Set.inter_comm. Hover over open if you want to learn more.

open MeasureTheory ProbabilityTheory Set

We define a measure space Ω: the MeasureSpace Ω variable states that Ω is a measurable space on which there is a canonical measure volume, with notation ℙ. We then state that ℙ is a probability measure. That is, ℙ univ = 1, where univ : Set Ω is the universal set in Ω (the set that contains all x : Ω).

variable {Ω : Type} [MeasureSpace Ω] [IsProbabilityMeasure (ℙ : Measure Ω)]

-- `A, B` will denote sets in `Ω`.
variable {A B : Set Ω}

One can take the measure of a set A: ℙ A : ℝ≥0∞. ℝ≥0∞, or ENNReal, is the type of extended non-negative real numbers, which contain ∞. Measures can in general take infinite values, but since our ℙ is a probability measure, it actually takes only values up to 1. simp knows that a probability measure is finite and will use the lemmas measure_ne_top or measure_lt_top to prove that ℙ A ≠ ∞ or ℙ A < ∞.

Hint: use #check measure_ne_top to see what that lemma does.

The operations on ENNReal are not as nicely behaved as on ℝ: ENNReal is not a ring and subtraction truncates to zero for example. If you find that lemma lemma_name used to transform an equation does not apply to ENNReal, try to find a lemma named something like ENNReal.lemma_name_of_something and use that instead.

  • Two sets A, B are independent for the ambient probability measure ℙ if ℙ (A ∩ B) = ℙ A * ℙ B.
def IndepSet (A B : Set Ω) : Prop := ℙ (A ∩ B) = ℙ A * ℙ B
  • If A is independent of B, then B is independent of A.
lemma IndepSet.symm : IndepSet A B → IndepSet B A := by
  sorry

Many lemmas in measure theory require sets to be measurable (MeasurableSet A). If you are presented with a goal like ⊢ MeasurableSet (A ∩ B), try the measurability tactic. That tactic produces measurability proofs.

-- Hints: `compl_eq_univ_diff`, `measure_diff`, `inter_univ`, `measure_compl`, `ENNReal.mul_sub`
lemma IndepSet.compl_right (hA : MeasurableSet A) (hB : MeasurableSet B) :
    IndepSet A B → IndepSet A Bᶜ := by
  sorry

Apply IndepSet.compl_right to prove this generalization. It is good practice to add the iff version of some frequently used lemmas, this allows us to use them inside rw tactics.

lemma IndepSet.compl_right_iff (hA : MeasurableSet A) (hB : MeasurableSet B) :
    IndepSet A Bᶜ ↔ IndepSet A B := by
  sorry

-- Use what you have proved so far
lemma IndepSet.compl_left (hA : MeasurableSet A) (hB : MeasurableSet B) (h : IndepSet A B) :
    IndepSet Aᶜ B := by
  sorry

Can you write and prove a lemma IndepSet.compl_left_iff, following the examples above?

-- Your lemma here

-- Hint: `ENNReal.mul_self_eq_self_iff`
lemma indep_self (h : IndepSet A A) : ℙ A = 0 ∨ ℙ A = 1 := by
  sorry

Conditional probability

  • The probability of set A conditioned on B.
def condProb (A B : Set Ω) : ENNReal := ℙ (A ∩ B) / ℙ B

We define a notation for condProb A B that makes it look more like paper math.

notation3 "ℙ("A"|"B")" => condProb A B

Now that we have defined condProb, we want to use it, but Lean knows nothing about it. We could start every proof with rw [condProb], but it is more convenient to write lemmas about the properties of condProb first and then use those.

-- Hint : `measure_inter_null_of_null_left`
@[simp] -- this makes the lemma usable by `simp`
lemma condProb_zero_left (A B : Set Ω) (hA : ℙ A = 0) : ℙ(A|B) = 0 := by
  sorry

@[simp]
lemma condProb_zero_right (A B : Set Ω) (hB : ℙ B = 0) : ℙ(A|B) = 0 := by
  sorry

What other basic lemmas could be useful? Are there other "special" sets for which condProb takes known values?

-- Your lemma(s) here

The next statement is a theorem and not a lemma, because we think it is important. There is no functional difference between those two keywords.

  • Bayes Theorem
theorem bayesTheorem (hB : ℙ B ≠ 0) : ℙ(A|B) = ℙ A * ℙ(B|A) / ℙ B := by
  by_cases h : ℙ A = 0 -- this tactic perfoms a case disjunction.
  -- Observe the goals that are created, and specifically the `h` assumption in both goals
  · sorry
  sorry

Did you really need all those hypotheses? In Lean, division by zero follows the convention that a/0 = 0 for all a. This means we can prove Bayes' Theorem without requiring ℙ A ≠ 0 and ℙ B ≠ 0. However, this is a quirk of the formalization rather than the standard mathematical statement. If you want to know more about how division works in Lean, try to hover over / with your mouse.

theorem bayesTheorem' (A B : Set Ω) : ℙ(A|B) = ℙ A * ℙ(B|A) / ℙ B := by
  sorry

lemma condProb_of_indepSet (h : IndepSet B A) (hB : ℙ B ≠ 0) : ℙ(A|B) = ℙ A := by
  sorry