Kernels from weight functions #
This file defines ProbabilityTheory.Kernel.ofWeights, the kernel that normalizes a
nonnegative weight function on a finite target into a probability measure per row, and
evaluates it at singletons. A row of zero or infinite total weight collapses to the zero
measure, so the kernel is always finite; it is Markov exactly on rows with a positive finite
total.
Main definitions #
ProbabilityTheory.Kernel.ofWeights— rowais proportional tow a.
Main results #
ProbabilityTheory.Kernel.ofWeights_apply_singleton—w a b / ∑ b', w a b';ofWeights_apply_finset,ofWeights_real_setOffor finite events.ProbabilityTheory.Kernel.ofWeights_real_singleton_lt_iff— row preference is weight comparison.ProbabilityTheory.Kernel.ofWeights_uniformOn_mul_uniformOn— the product of two uniform experts is uniform on their agreement set.
The kernel that normalizes a nonnegative weight function on a finite target has row a the
probability measure proportional to w a. A row of zero or infinite total weight collapses to
the zero measure.
Equations
- ProbabilityTheory.Kernel.ofWeights w = ProbabilityTheory.Kernel.ofFunOfCountable fun (a : α) => (∑ b : β, w a b)⁻¹ • ∑ b : β, w a b • MeasureTheory.Measure.dirac b
Instances For
The mass of a finite event under a weight-kernel row.
The real mass of a decidable event under a weight-kernel row with finite weights.
Row-preference in a weight kernel reduces to weight comparison; the normalization cancels.
The product of two uniform experts is uniform on their agreement set, and collapses to the zero measure when they agree nowhere.