Filtration built from the finite partitions of a countably generated measurable space #
In a countably generated measurable space α, we can build a sequence of finer and finer finite
measurable partitions of the space such that the measurable space is generated by the union of all
partitions.
This sequence of partitions is defined in MeasureTheory.MeasurableSpace.CountablyGenerated.
Here, we build the filtration of the measurable spaces generated by countablePartition α n for all
n : ℕ, which we call countableFiltration α.
Since each measurable space in the filtration is finite, we can easily build measurable functions on
those spaces. A potential application of countableFiltration α is to build a martingale with
respect to that filtration and use the martingale convergence theorems to define a measurable
function on α.
Main definitions #
- ProbabilityTheory.partitionFiltration: for a sequence of sets- t : ℕ → Set α, a filtration built from the measurable spaces generated by- memPartition t nfor all- n : ℕ.
- ProbabilityTheory.countableFiltration: A filtration built from the measurable spaces generated by- countablePartition α nfor all- n : ℕ.
Main statements #
- ProbabilityTheory.iSup_partitionFiltration:- ⨆ n, partitionFiltration α nis the measurable space on- α.
A filtration built from the measurable spaces generated by the partitions memPartition t n for
all n : ℕ.
Equations
- ProbabilityTheory.partitionFiltration ht = { seq := fun (n : ℕ) => MeasurableSpace.generateFrom (memPartition t n), mono' := ⋯, le' := ⋯ }
Instances For
A filtration built from the measurable spaces generated by countablePartition α n for
all n : ℕ.
Equations
- ProbabilityTheory.countableFiltration α = { seq := fun (n : ℕ) => MeasurableSpace.generateFrom (MeasurableSpace.countablePartition α n), mono' := ⋯, le' := ⋯ }