KSLCore/ksl.utilities.distributions.fitting.scoring/AkaikeInfoCriterionScoringModel

AkaikeInfoCriterionScoringModel

class AkaikeInfoCriterionScoringModel(val domain: Interval = DEFAULT_BIG_RANGE) : PDFScoringModel

Computes the Akaike Information Criterion (AIC) based on the data as the score. This assumes that the parameters of the supplied distribution have been estimated from the data and evaluates the likelihood associated with the current parameters of the distribution. The parameters of the distribution are not assumed to have been estimated from a maximum likelihood approach.

Constructors

AkaikeInfoCriterionScoringModel

constructor(domain: Interval = DEFAULT_BIG_RANGE)

Properties

allowLowerLimitAdjustment

open override val allowLowerLimitAdjustment: Boolean = true

Indicates if the lower limit of the domain may be adjusted during scaling processes

allowUpperLimitAdjustment

open override val allowUpperLimitAdjustment: Boolean = true

Indicates if the upper limit of the domain may be adjusted during scaling processes

open override var description: String?

open override var direction: MetricIfc.Direction

override val domain: Interval

open override val name: String

open override var unitsOfMeasure: String?

Functions

open fun badScore(): Score

Returns a valid score that has the worst possible value according to the direction of the meaning of better.

open fun metricData(modaName: String, weight: Double): MetricData

Returns an instance of MetricData based on (modaName, weight)

open override fun newInstance(): AkaikeInfoCriterionScoringModel

open override fun score(data: DoubleArray, cdf: ContinuousDistributionIfc): Score

fun score(result: EstimationResult): Score

fun score(data: DoubleArray, parameters: RVParameters): Score

open override fun toString(): String