Learning.obs_bayesStationaryEnv_zero
Lemma
No docstring.
Types
-
𝓔 : Type u_1MeasurableSpace 𝓔A measurable space is a space equipped with a σ-algebra. -
𝓐 : Type u_2MeasurableSpace 𝓐 -
𝓨 : Type u_3MeasurableSpace 𝓨
Given
-
Q : MeasureTheory.Measure 𝓔A measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure.MeasureTheory.IsProbabilityMeasure QA measureμis called a probability measure ifμ univ = 1. -
κ : ProbabilityTheory.Kernel (𝓔 × 𝓐) 𝓨A kernel from a measurable spaceαto another measurable spaceβis a measurable functionκ : α → Measure β.ProbabilityTheory.IsMarkovKernel κA kernel is a Markov kernel if every measure in its image is a probability measure.
Then
(bayesStationaryEnv Q κ).obs 0 = ProbabilityTheory.Kernel.const (Hist 𝓔 𝓐 𝓨 0) QMeasurableSpace : Type u_6 → Type u_6A measurable space is a space equipped with a σ-algebra.
MeasureTheory.IsProbabilityMeasure : {α : Type u_1} → {m0 : MeasurableSpace α} → MeasureTheory.Measure α → PropA measure `μ` is called a probability measure if `μ univ = 1`.
MeasureTheory.Measure : (α : Type u_5) → [MeasurableSpace α] → Type u_5A measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure. The measure of a set `s`, denoted `μ s`, is an extended nonnegative real. The real-valued version is written `μ.real s`.
ProbabilityTheory.IsMarkovKernel : {α : Type u_1} →
{β : Type u_2} → {mα : MeasurableSpace α} → {mβ : MeasurableSpace β} → ProbabilityTheory.Kernel α β → PropA kernel is a Markov kernel if every measure in its image is a probability measure.
ProbabilityTheory.Kernel : (α : Type u_1) → (β : Type u_2) → [MeasurableSpace α] → [MeasurableSpace β] → Type (max u_1 u_2)A kernel from a measurable space `α` to another measurable space `β` is a measurable function `κ : α → Measure β`. The measurable space structure on `MeasureTheory.Measure β` is given by `MeasureTheory.Measure.instMeasurableSpace`. A map `κ : α → MeasureTheory.Measure β` is measurable iff `∀ s : Set β, MeasurableSet s → Measurable (fun a ↦ κ a s)`.
Prod : Type u → Type v → Type (max u v)The product type, usually written `α × β`. Product types are also called pair or tuple types. Elements of this type are pairs in which the first element is an `α` and the second element is a `β`. Products nest to the right, so `(x, y, z) : α × β × γ` is equivalent to `(x, (y, z)) : α × (β × γ)`. Conventions for notations in identifiers: * The recommended spelling of `×` in identifiers is `Prod`.
Eq : {α : Sort u_1} → α → α → PropThe equality relation. It has one introduction rule, `Eq.refl`.
We use `a = b` as notation for `Eq a b`.
A fundamental property of equality is that it is an equivalence relation.
```
variable (α : Type) (a b c d : α)
variable (hab : a = b) (hcb : c = b) (hcd : c = d)
example : a = d :=
Eq.trans (Eq.trans hab (Eq.symm hcb)) hcd
```
Equality is much more than an equivalence relation, however. It has the important property that every assertion
respects the equivalence, in the sense that we can substitute equal expressions without changing the truth value.
That is, given `h1 : a = b` and `h2 : p a`, we can construct a proof for `p b` using substitution: `Eq.subst h1 h2`.
Example:
```
example (α : Type) (a b : α) (p : α → Prop)
(h1 : a = b) (h2 : p a) : p b :=
Eq.subst h1 h2
example (α : Type) (a b : α) (p : α → Prop)
(h1 : a = b) (h2 : p a) : p b :=
h1 ▸ h2
```
The triangle in the second presentation is a macro built on top of `Eq.subst` and `Eq.symm`, and you can enter it by typing `\t`.
For more information: [Equality](https://lean-lang.org/theorem_proving_in_lean4/quantifiers_and_equality.html#equality)
Conventions for notations in identifiers:
* The recommended spelling of `=` in identifiers is `eq`.Learning.bayesStationaryEnv : {𝓔 : Type u_1} →
{𝓐 : Type u_2} →
{𝓨 : Type u_3} →
[inst : MeasurableSpace 𝓔] →
[inst_1 : MeasurableSpace 𝓐] →
[inst_2 : MeasurableSpace 𝓨] →
(Q : MeasureTheory.Measure 𝓔) →
[MeasureTheory.IsProbabilityMeasure Q] →
(κ : ProbabilityTheory.Kernel (𝓔 × 𝓐) 𝓨) →
[ProbabilityTheory.IsMarkovKernel κ] → Learning.Envi…The environment that draws a parameter `e : 𝓔` from `Q` before the first round, announces it as the observation of every round, and returns a feedback drawn from `κ (e, a)` when the action is `a`. The algorithm is meant to ignore the announced parameter, that is, to run through `Algorithm.comapObs (fun _ ↦ ())`.Go to its page
ProbabilityTheory.Kernel.const : (α : Type u_4) →
{β : Type u_5} →
[inst : MeasurableSpace α] → {x : MeasurableSpace β} → MeasureTheory.Measure β → ProbabilityTheory.Kernel α βConstant kernel, which always returns the same measure.
Learning.Hist : Type u_5 → Type u_6 → Type u_7 → ℕ → Type (max (max u_7 u_6) u_5)History of `n` complete rounds; `n = 0` is the empty history.Go to its page
Code
lemma obs_bayesStationaryEnv_zero : (bayesStationaryEnv Q κ).obs 0 = Kernel.const _ Q
Proof
rfl
Meaning last changed in v4.34.0-rc2-76-g565f652 (2026-09-10).
Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file · open it in the Lean web editor.
Dependency graph
Audit surface: 5 project declarations, 27 external constants
✓ Proved: no sorry anywhere in its closure
This is the tool's own reading of one build's recorded axioms, and it is not robust against an author who wants it to pass. Checking meant to be relied on should go through Comparator, which replays the proof through the kernel from an export against an explicit list of permitted axioms.