v3-ProbabilityDistributionType
type: CodeSystem
node role: treeRoot
id:
  v: v3-ProbabilityDistributionType
language:
  v: en
text:
  status:
    v: generated
  div: Properties This code system defines the following properties for its concepts Code URI Type Description Specializes Coding The child code is a more narrow version of the concept represented by the parent code. I.e. Every child concept is also a valid parent concept. Used to allow determination of subsumption. Must be transitive, irreflexive, antisymmetric. Generalizes Coding Inverse of Specializes. Only included as a derived relationship. internalId http://terminology.hl7.org/CodeSystem/utg-concept-properties#v3-internal-id code The internal identifier for the concept in the HL7 Access database repository. status http://hl7.org/fhir/concept-properties#status code Designation of a concept's state. Normally is not populated unless the state is retired. Concepts This case-sensitive code system http://terminology.hl7.org/CodeSystem/v3-ProbabilityDistributionType defines the following codes in a Is-A hierarchy: Code Display Definition internalId status B beta The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g., occurs when probabilities are estimated.) Two parameters a and b are available to adjust the curve. The mean m and variance s2 relate as follows: m = a/ (a + b) and s2 = ab/((a + b)2 (a + b + 1)). 10756 active E exponential Used for data that describes extinction. The exponential distribution is a special form of g-distribution where a = 1, hence, the relationship to mean m and variance s2 are m = b and s2 = b2. 10752 active F F Used to describe the quotient of two c2 random variables. The F-distribution has two parameters n1 and n2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean m and variance s2 are: m = n2 / (n2 - 2) and s2 = (2 n2 (n2 + n1 - 2)) / (n1 (n2 - 2)2 (n2 - 4)). 10755 active G (gamma) The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located ne…
url:
  v: http://terminology.hl7.org/CodeSystem/v3-ProbabilityDistributionType
identifier:
  system:
    v: urn:ietf:rfc:3986
  value:
    v: urn:oid:2.16.840.1.113883.5.1020
version:
  v: 3.0.0
name:
  v: ProbabilityDistributionType
title:
  v: ProbabilityDistributionType
status:
  v: active
experimental:
  v: false
date:
  v: 2019-03-20
publisher:
  v: Health Level Seven International
contact:
  telecom:
    system:
      v: url
    value:
      v: http://hl7.org
  telecom:
    system:
      v: email
description:
  v: **** MISSING DESCRIPTION ****
copyright:
  v: This material derives from the HL7 Terminology THO. THO is copyright ©1989+ Health Level Seven International and is made available under the CC0 designation. For more licensing information see: https://terminology.hl7.org/license
case sensitive:
  v: true
hierarchy meaning:
  v: is-a
content:
  v: complete
property:
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-symmetry
    value:
      v: antisymmetric
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-transitivity
    value:
      v: transitive
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-reflexivity
    value:
      v: irreflexive
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-isNavigable
    value:
      v: true
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-relationshipKind
    value:
      v: Specializes
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-inverseName
    value:
      v: Generalizes
  code:
    v: Specializes
  description:
    v: The child code is a more narrow version of the concept represented by the parent code. I.e. Every child concept is also a valid parent concept. Used to allow determination of subsumption. Must be transitive, irreflexive, antisymmetric.
  type:
    v: Coding
property:
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-symmetry
    value:
      v: antisymmetric
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-transitivity
    value:
      v: transitive
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-reflexivity
    value:
      v: irreflexive
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-isNavigable
    value:
      v: true
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-relationshipKind
    value:
      v: Generalizes
  extension:
    url:
      v: http://terminology.hl7.org/StructureDefinition/ext-mif-relationship-inverseName
    value:
      v: Specializes
  code:
    v: Generalizes
  description:
    v: Inverse of Specializes. Only included as a derived relationship.
  type:
    v: Coding
property:
  code:
    v: internalId
  uri:
    v: http://terminology.hl7.org/CodeSystem/utg-concept-properties#v3-internal-id
  description:
    v: The internal identifier for the concept in the HL7 Access database repository.
  type:
    v: code
property:
  code:
    v: status
  uri:
    v: http://hl7.org/fhir/concept-properties#status
  description:
    v: Designation of a concept's state. Normally is not populated unless the state is retired.
  type:
    v: code
concept:
  code:
    v: B
  display:
    v: beta
  definition:
    v: The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g., occurs when probabilities are estimated.) Two parameters a and b are available to adjust the curve. The mean m and variance s2 relate as follows: m = a/ (a + b) and s2 = ab/((a + b)2 (a + b + 1)).
  property:
    code:
      v: status
    value:
      v: active
  property:
    code:
      v: internalId
    value:
      v: 10756
concept:
  code:
    v: E
  display:
    v: exponential
  definition:
    v: Used for data that describes extinction. The exponential distribution is a special form of g-distribution where a = 1, hence, the relationship to mean m and variance s2 are m = b and s2 = b2.
  property:
    code:
      v: status
    value:
      v: active
  property:
    code:
      v: internalId
    value:
      v: 10752
concept:
  code:
    v: F
  display:
    v: F
  definition:
    v: Used to describe the quotient of two c2 random variables. The F-distribution has two parameters n1 and n2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean m and variance s2 are: m = n2 / (n2 - 2) and s2 = (2 n2 (n2 + n1 - 2)) / (n1 (n2 - 2)2 (n2 - 4)).
  property:
    code:
      v: status
    value:
      v: active
  property:
    code:
      v: internalId
    value:
      v: 10755
concept:
  code:
    v: G
  display:
    v: (gamma)
  definition:
    v: The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located near the origin. The g-distribution has a two parameters a and b. The relationship to mean m and variance s2 is m = a b and s2 = a b2.
  property:
    code:
      v: status

v: active

code:
  v: internalId
value:
  v: 10751

code:
  v: LN
display:
  v: log-normal
definition:
  v: The logarithmic normal distribution is used to transform skewed random variable X into a normally distributed random variable U = log X. The log-normal distribution can be specified with the properties mean m and standard deviation s. Note however that mean m and standard deviation s are the parameters of the raw value distribution, not the transformed parameters of the lognormal distribution that are conventionally referred to by the same letters. Those log-normal parameters mlog and slog relate to the mean m and standard deviation s of the data value through slog2 = log (s2/m2 + 1) and mlog = log m - slog2/2.
property:
  code:
    v: status
  value:
    v: active
property:
  code:
    v: internalId
  value:
    v: 10750

code:
  v: N
display:
  v: normal (Gaussian)
definition:
  v: This is the well-known bell-shaped normal distribution. Because of the central limit theorem, the normal distribution is the distribution of choice for an unbounded random variable that is an outcome of a combination of many stochastic processes. Even for values bounded on a single side (i.e. greater than 0) the normal distribution may be accurate enough if the mean is "far away" from the bound of the scale measured in terms of standard deviations.
property:
  code:
    v: status
  value:
    v: active
property:
  code:
    v: internalId
  value:
    v: 10749

code:
  v: T
display:
  v: T
definition:
  v: Used to describe the quotient of a normal random variable and the square root of a c2 random variable. The t-distribution has one parameter n, the number of degrees of freedom. The relationship to mean m and variance s2 are: m = 0 and s2 = n / (n - 2)
property:
  code:
    v: status
  value:
    v: active
property:
  code:
    v: internalId
  value:
    v: 10754

code:
  v: U
display:
  v: uniform
definition:
  v: The uniform distribution assigns a constant probability over the entire interval of possible outcomes, while all outcomes outside this interval are assumed to have zero probability. The width of this interval is 2s sqrt(3). Thus, the uniform distribution assigns the probability densities f(x) = sqrt(2 s sqrt(3)) to values m - s sqrt(3) >= x <= m + s sqrt(3) and f(x) = 0 otherwise.
property:
  code:
    v: status
  value:
    v: active
property:
  code:
    v: internalId
  value:
    v: 10748

code:
  v: X2
display:
  v: chi square
definition:
  v: Used to describe the sum of squares of random variables which occurs when a variance is estimated (rather than presumed) from the sample. The only parameter of the c2-distribution is n, so called the number of degrees of freedom (which is the number of independent parts in the sum). The c2-distribution is a special type of g-distribution with parameter a = n /2 and b = 2. Hence, m = n and s2 = 2 n.
property:
  code:
    v: status
  value:
    v: active
property:
  code:
    v: internalId
  value:
    v: 10753