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water-filling concept

DefinitionThe allocation of a bit budget across directions that gives each direction half the log of its sensitivity-weighted variance over a common water level, and nothing to directions under the water. Equation 0.13.
ExampleDirections with variances 4, 1, and 0.1 and a water level of 0.5 get bits in proportion to the log of 8, the log of 2, and nothing.
BookData Mining as Observation, draft 0.2, commit f3914f0; entry id water-filling, kind concept.
Statusno ledger row names this entry. Corrections: none recorded.
Defining equationnone
Assumptions and scope
  • The closed form assumes a uniform quantizer at high rate, so that each bit quarters the squared error, and directions taken as the eigenvectors of the covariance so that their errors add.
  • The sensitivity of each direction is read in that same eigenbasis, as the quadratic form of the read operator along the eigenvector, and pairing the eigenvalues of the two matrices is valid only when they share an eigenbasis.
  • Coarse quantizers, unequal codebook steps, and errors not spread evenly depart from the formula, and chapter 4 reports where a measured allocation did.
Prior artReverse water-filling of rate-distortion theory, per Cover and Thomas, applied to the read-weighted spectrum.
Evidencereadscope/readscope/allocate.py:1-100, lean/DataMiningAsObservation/WaterFilling.lean
Reviewedsemantic review 2026-09-06; generated 2026-09-10 from records at the commits on the provenance page.
directioneigenvaluewater level
Directions below the water get no bits.

Equation

none

Conditions

Conditions are curated in entries.toml rather than read from a record.

Ledger

none

First stated

Shannon’s power allocation across channels, applied with a consumer’s sensitivity in place of a signal’s power in readscope and turboquant-pro, and chapter 0 section 0.7 and chapter 4 section 4.2 of Data Mining as Observation.

Measurements

Where the book states it Numbers, as the book’s sources table records them Source
chapter 4 section 4.2 water-filling formula, directions below the water get no bits, the surrogate caveat readscope/readscope/allocate.py:1-100

Failures and corrections

none

Invariance envelope

none declared

Machine checked

lean/DataMiningAsObservation/WaterFilling.lean, theorems contribution_eq_water, terms_mul, two_sqrt_le, distortion2_ge, distortion2_eq_of_equal, at observation-data-mining f3914f0; what the check covers is stated in the book’s appendix C.

Used in

Data Mining as Observation chapters 0, 4.

Related

read operator; read distortion; alignment; the flip.

See also

Book equations stated beside the entry’s terms, not defining it: 0.13, 4.2.

Sources-table rows that share a record with the entry without naming it: chapter 2 section 2.3, chapter 2 section 2.4, chapter 2 section 2.6, chapter 3 section 3.4, chapter 4 section 4.2, chapter 4 section 4.4, chapter 4 section 4.5, chapter 6 section 6.1, chapter 8 section 8.3, chapter 8 section 8.4, chapter 11 section 11.7, chapter 11 section 11.9.

Status

Generated 2026-09-10 by encyclopedia/generate.py; book at observation-data-mining f3914f0; the commit of every record is listed in the encyclopedia’s provenance.

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