Slot: covariance

Covariance of the unit-intensity noise increment across the axis named by correlated_over — the second-order structure of the driving process, stated as mathematics rather than as a factorisation. Must be square in that axis, symmetric and positive semi-definite; its diagonal carries per-element variances and need NOT be unity, so unequal variances and cross-element correlation are expressed together. The realised increment is the declared sigma times a draw from this covariance, so parameters: {sigma: ...} continues to mean what it means for uncorrelated noise. Absent (the default) means the increment is independent across that axis. Being a Parameter, it carries the full provenance triple: a literal value:, existing bytes via source:+measure:, or — the usual case for a derived operator — producer:, whose FunctionCall records how the structure was obtained (projecting a spatially white field into a modal basis, spreading independent per-region drives through a connectome). The generative account therefore lives in the provenance, and the matrix itself stays a statement about the process. How a backend samples it (Cholesky, eigendecomposition, whitening) is a backend concern and is never stated here.

URI: tvbo:slot/covariance

Applicable Classes

Name Description Modifies Slot
Noise no

Properties

Type and Range

Property Value
Range Parameter
Domain Of Noise

Cardinality and Requirements

Property Value

Slot Characteristics

Property Value
Owner Noise

Identifier and Mapping Information

Schema Source

  • from schema: https://w3id.org/tvbo

Mappings

Mapping Type Mapped Value
self tvbo:covariance
native tvbo:covariance

LinkML Source

name: covariance
description: 'Covariance of the unit-intensity noise increment across the axis named
  by `correlated_over` — the second-order structure of the driving process, stated
  as mathematics rather than as a factorisation. Must be square in that axis, symmetric
  and positive semi-definite; its diagonal carries per-element variances and need
  NOT be unity, so unequal variances and cross-element correlation are expressed together.
  The realised increment is the declared sigma times a draw from this covariance,
  so `parameters: {sigma: ...}` continues to mean what it means for uncorrelated noise.
  Absent (the default) means the increment is independent across that axis. Being
  a `Parameter`, it carries the full provenance triple: a literal `value:`, existing
  bytes via `source:`+`measure:`, or — the usual case for a derived operator — `producer:`,
  whose FunctionCall records how the structure was obtained (projecting a spatially
  white field into a modal basis, spreading independent per-region drives through
  a connectome). The generative account therefore lives in the provenance, and the
  matrix itself stays a statement about the process. How a backend samples it (Cholesky,
  eigendecomposition, whitening) is a backend concern and is never stated here.'
from_schema: https://w3id.org/tvbo
rank: 1000
owner: Noise
domain_of:
- Noise
range: Parameter
required: false
inlined: true