LeanMachineLearning

Learning.IT.instIsMarkovKernelHistUnitBayesTrajMeasurePosterioršŸ”—

Instance

No docstring.

Types
  • š“” : Type u_3MeasurableSpace š“”A measurable space is a space equipped with a σ-algebra.StandardBorelSpace š“”A standard Borel space is a measurable space arising as the Borel sets of some Polish topology.Nonempty š“”
  • š“ : Type u_1MeasurableSpace š“
  • š“Ø : Type u_2MeasurableSpace š“Ø
Given
  • Q : MeasureTheory.Measure š“”A 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 measure μ is called a probability measure if μ univ = 1.
  • Īŗ : ProbabilityTheory.Kernel (š“” Ɨ š“) š“ØA kernel from a measurable space α to another measurable space β is a measurable function Īŗ : α → Measure β.ProbabilityTheory.IsMarkovKernel ĪŗA kernel is a Markov kernel if every measure in its image is a probability measure.
  • alg : Algorithm Unit š“ š“ØA stochastic, sequential algorithm.
  • n : ā„•
Then
ProbabilityTheory.IsMarkovKernel (bayesTrajMeasurePosterior Q Īŗ alg n)
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

āœ“ 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.