production module
AutoSamplingTheory.TechnicalLemmas.Probability.KernelReversibility
1 named declarations scanned from AutoSamplingTheory/TechnicalLemmas/Probability/KernelReversibility.lean.
Declarations
theorem AutoSamplingTheory.TechnicalLemmas.Probability.KernelReversibility.isReversible_of_singleton_balance Partial Not mapped
- Atomic detailed balance implies equality of the two set flux integrals. The given measure and kernel need not have finite total mass.
theorem isReversible_of_singleton_balance {α : Type*} [MeasurableSpace α]
[Countable α] [MeasurableSingletonClass α] (κ : Kernel α α) (μ : Measure α)
(h : ∀ x y, μ {x} * κ x {y} = μ {y} * κ y {x}) :
Kernel.IsReversible κ μ := by
intro A B _ _
have hm (ν : Measure α) (s : Set α) : ν s = ∑' y : s, ν {(y : α)} := by
simpa using (lintegral_countable (μ := ν) (fun _ => 1) (Set.to_countable s))
rw [lintegral_countable _ (Set.to_countable A),
lintegral_countable _ (Set.to_countable B)]
conv_lhs => enter [1, x]; rw [hm (κ x) B, ← ENNReal.tsum_mul_right]
conv_rhs => enter [1, y]; rw [hm (κ y) A, ← ENNReal.tsum_mul_right]
rw [ENNReal.tsum_comm]
apply tsum_congr
intro y
apply tsum_congr
intro x
simpa only [mul_comm] using h (x : α) (y : α)
end AutoSamplingTheory.TechnicalLemmas.Probability.KernelReversibility
AutoSamplingTheory/TechnicalLemmas/Probability/KernelReversibility.lean:19published source at 0e31a3cda412
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Mathematical derivations with optional Lean and source details.