Learning.IT.instIsMarkovKernelHistUnitBayesTrajMeasurePosterior
Instance
No docstring.
Types
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š : Type u_3MeasurableSpace šA measurable space is a space equipped with a Ļ-algebra.StandardBorelSpace šA standard Borel space is a measurable space arising as the Borel sets of some Polish topology.Nonempty š -
š : Type u_1MeasurableSpace š -
šØ : Type u_2MeasurableSpace šØ
Given
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Q : MeasureTheory.Measure šA 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. -
Īŗ : ProbabilityTheory.Kernel (š Ć š) šØA kernel from a measurable spaceαto another measurable spaceβis a measurable functionĪŗ : α ā Measure β.ProbabilityTheory.IsMarkovKernel ĪŗA kernel is a Markov kernel if every measure in its image is a probability measure. -
alg : Algorithm Unit š šØA stochastic, sequential algorithm. -
n : ā
Then
ProbabilityTheory.IsMarkovKernel (bayesTrajMeasurePosterior Q Īŗ alg n)MeasurableSpace : Type u_6 ā Type u_6A measurable space is a space equipped with a Ļ-algebra.
StandardBorelSpace : (α : Type u_1) ā [MeasurableSpace α] ā PropA standard Borel space is a measurable space arising as the Borel sets of some Polish topology. This is useful in situations where a space has no natural topology or the natural topology in a space is non-Polish. To endow a standard Borel space `α` with a compatible Polish topology, use `letI := upgradeStandardBorel α`. One can then use `eq_borel_upgradeStandardBorel α` to rewrite the `MeasurableSpace α` instance to `borel α t`, where `t` is the new topology.
Nonempty : Sort u ā Prop`Nonempty α` is a typeclass that says that `α` is not an empty type, that is, there exists an element in the type. It differs from `Inhabited α` in that `Nonempty α` is a `Prop`, which means that it does not actually carry an element of `α`, only a proof that *there exists* such an element. Given `Nonempty α`, you can construct an element of `α` *nonconstructively* using `Classical.choice`.
MeasureTheory.IsProbabilityMeasure : {α : Type u_1} ā {m0 : MeasurableSpace α} ā MeasureTheory.Measure α ā PropA measure `μ` is called a probability measure if `μ univ = 1`.
MeasureTheory.Measure : (α : Type u_5) ā [MeasurableSpace α] ā 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} ā
{β : Type u_2} ā {mα : MeasurableSpace α} ā {mβ : MeasurableSpace β} ā ProbabilityTheory.Kernel α β ā PropA kernel is a Markov kernel if every measure in its image is a probability measure.
ProbabilityTheory.Kernel : (α : Type u_1) ā (β : Type u_2) ā [MeasurableSpace α] ā [MeasurableSpace β] ā Type (max u_1 u_2)A kernel from a measurable space `α` to another measurable space `β` is a measurable function `Īŗ : α ā Measure β`. The measurable space structure on `MeasureTheory.Measure β` is given by `MeasureTheory.Measure.instMeasurableSpace`. A map `Īŗ : α ā MeasureTheory.Measure β` is measurable iff `ā s : Set β, MeasurableSet s ā Measurable (fun a ⦠κ a s)`.
Prod : Type u ā Type v ā Type (max u v)The product type, usually written `α à 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 à β à γ` is equivalent to `(x, (y, z)) : α Ć (β à γ)`. Conventions for notations in identifiers: * The recommended spelling of `Ć` in identifiers is `Prod`.
Learning.Algorithm : (š : Type u_5) ā
(š : Type u_6) ā
(šØ : Type u_7) ā [MeasurableSpace š] ā [MeasurableSpace š] ā [MeasurableSpace šØ] ā Type (max (max u_5 u_6) u_7)A stochastic, sequential algorithm. At each round, it sees an observation in `š`, then takes an action in `š`, and finally receives feedback in `šØ`. The action is a random function of the past rounds and the current observation.Go to its page
Unit : TypeThe canonical type with one element. This element is written `()`. `Unit` has a number of uses: * It can be used to model control flow that returns from a function call without providing other information. * Monadic actions that return `Unit` have side effects without computing values. * In polymorphic types, it can be used to indicate that no data is to be stored in a particular field.
Nat : TypeThe natural numbers, starting at zero. This type is special-cased by both the kernel and the compiler, and overridden with an efficient implementation. Both use a fast arbitrary-precision arithmetic library (usually [GMP](https://gmplib.org/)); at runtime, `Nat` values that are sufficiently small are unboxed.
Learning.IT.bayesTrajMeasurePosterior : {š : Type u_1} ā
{š : Type u_2} ā
{šØ : Type u_3} ā
[inst : MeasurableSpace š] ā
[inst_1 : MeasurableSpace š] ā
[inst_2 : MeasurableSpace šØ] ā
[StandardBorelSpace š] ā
[Nonempty š] ā
(Q : MeasureTheory.Measure š) ā
[MeasureTheory.IsProbabilityMeasure Q] ā
(Īŗ : ProbabilityTheory.Kernel (š Ćā¦A kernel that represents the posterior over `E` given the history before time `n`.Go to its page
Code
deriving IsMarkovKernel
Proof
deriving IsMarkovKernel
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 Ā· open it in the Lean web editor.
Dependency graph
Audit surface: 33 project declarations, 81 external constants
ā 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.