Documentation ¶
Index ¶
- type DataComparisonIteration
- type DataGenerationIteration
- type GammaLikelihoodDistribution
- func (g *GammaLikelihoodDistribution) Configure(partitionIndex int, settings *simulator.Settings)
- func (g *GammaLikelihoodDistribution) EvaluateLogLike(mean *mat.VecDense, covariance mat.Symmetric, data []float64) float64
- func (g *GammaLikelihoodDistribution) GenerateNewSamples(mean *mat.VecDense, covariance mat.Symmetric) []float64
- type LikelihoodDistribution
- type NegativeBinomialLikelihoodDistribution
- func (n *NegativeBinomialLikelihoodDistribution) Configure(partitionIndex int, settings *simulator.Settings)
- func (n *NegativeBinomialLikelihoodDistribution) EvaluateLogLike(mean *mat.VecDense, covariance mat.Symmetric, data []float64) float64
- func (n *NegativeBinomialLikelihoodDistribution) GenerateNewSamples(mean *mat.VecDense, covariance mat.Symmetric) []float64
- type NormalLikelihoodDistribution
- func (n *NormalLikelihoodDistribution) Configure(partitionIndex int, settings *simulator.Settings)
- func (n *NormalLikelihoodDistribution) EvaluateLogLike(mean *mat.VecDense, covariance mat.Symmetric, data []float64) float64
- func (n *NormalLikelihoodDistribution) GenerateNewSamples(mean *mat.VecDense, covariance mat.Symmetric) []float64
- type PoissonLikelihoodDistribution
- func (p *PoissonLikelihoodDistribution) Configure(partitionIndex int, settings *simulator.Settings)
- func (p *PoissonLikelihoodDistribution) EvaluateLogLike(mean *mat.VecDense, covariance mat.Symmetric, data []float64) float64
- func (p *PoissonLikelihoodDistribution) GenerateNewSamples(mean *mat.VecDense, covariance mat.Symmetric) []float64
- type PosteriorCovarianceIteration
- type PosteriorLogNormalisationIteration
- type PosteriorMeanIteration
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type DataComparisonIteration ¶
type DataComparisonIteration struct { Likelihood LikelihoodDistribution // contains filtered or unexported fields }
DataComparisonIteration allows for any data linking log-likelihood to be used as a comparison distribution between data values, a mean vector and covariance matrix.
func (*DataComparisonIteration) Configure ¶
func (d *DataComparisonIteration) Configure( partitionIndex int, settings *simulator.Settings, )
func (*DataComparisonIteration) Iterate ¶
func (d *DataComparisonIteration) Iterate( params *simulator.OtherParams, partitionIndex int, stateHistories []*simulator.StateHistory, timestepsHistory *simulator.CumulativeTimestepsHistory, ) []float64
type DataGenerationIteration ¶
type DataGenerationIteration struct { Likelihood LikelihoodDistribution // contains filtered or unexported fields }
DataGenerationIteration allows for any data-linking likelihood to be used as a data generation distribution based on a mean and covariance matrix.
func (*DataGenerationIteration) Configure ¶
func (d *DataGenerationIteration) Configure( partitionIndex int, settings *simulator.Settings, )
func (*DataGenerationIteration) Iterate ¶
func (d *DataGenerationIteration) Iterate( params *simulator.OtherParams, partitionIndex int, stateHistories []*simulator.StateHistory, timestepsHistory *simulator.CumulativeTimestepsHistory, ) []float64
type GammaLikelihoodDistribution ¶
GammaLikelihoodDistribution assumes the real data are well described by a gamma distribution, given the input mean and covariance matrix.
func (*GammaLikelihoodDistribution) Configure ¶
func (g *GammaLikelihoodDistribution) Configure( partitionIndex int, settings *simulator.Settings, )
func (*GammaLikelihoodDistribution) EvaluateLogLike ¶
func (*GammaLikelihoodDistribution) GenerateNewSamples ¶
type LikelihoodDistribution ¶
type LikelihoodDistribution interface { Configure(partitionIndex int, settings *simulator.Settings) EvaluateLogLike(mean *mat.VecDense, covariance mat.Symmetric, data []float64) float64 GenerateNewSamples(mean *mat.VecDense, covariance mat.Symmetric) []float64 }
LikelihoodDistribution is the interface that must be implemented in order to create a likelihood that connects derived statistics from the probabilistic reweighting to observed actual data values.
type NegativeBinomialLikelihoodDistribution ¶
NegativeBinomialLikelihoodDistribution assumes the real data are well described by a negative binomial distribution, given the input mean and covariance matrix.
func (*NegativeBinomialLikelihoodDistribution) Configure ¶
func (n *NegativeBinomialLikelihoodDistribution) Configure( partitionIndex int, settings *simulator.Settings, )
func (*NegativeBinomialLikelihoodDistribution) EvaluateLogLike ¶
func (*NegativeBinomialLikelihoodDistribution) GenerateNewSamples ¶
type NormalLikelihoodDistribution ¶
type NormalLikelihoodDistribution struct { Src rand.Source // contains filtered or unexported fields }
NormalLikelihoodDistribution assumes the real data are well described by a normal distribution, given the input mean and covariance matrix.
func (*NormalLikelihoodDistribution) Configure ¶
func (n *NormalLikelihoodDistribution) Configure( partitionIndex int, settings *simulator.Settings, )
func (*NormalLikelihoodDistribution) EvaluateLogLike ¶
func (*NormalLikelihoodDistribution) GenerateNewSamples ¶
type PoissonLikelihoodDistribution ¶
PoissonLikelihoodDistribution assumes the real data are well described by a Poisson distribution, given the input mean and covariance matrix.
func (*PoissonLikelihoodDistribution) Configure ¶
func (p *PoissonLikelihoodDistribution) Configure( partitionIndex int, settings *simulator.Settings, )
func (*PoissonLikelihoodDistribution) EvaluateLogLike ¶
func (*PoissonLikelihoodDistribution) GenerateNewSamples ¶
type PosteriorCovarianceIteration ¶
type PosteriorCovarianceIteration struct { }
PosteriorCovarianceIteration updates an estimate of the covariance matrix of the posterior distribution over params using log-likelihood and param values given in the state history of other partitions, and a mean vector.
func (*PosteriorCovarianceIteration) Configure ¶
func (p *PosteriorCovarianceIteration) Configure( partitionIndex int, settings *simulator.Settings, )
func (*PosteriorCovarianceIteration) Iterate ¶
func (p *PosteriorCovarianceIteration) Iterate( params *simulator.OtherParams, partitionIndex int, stateHistories []*simulator.StateHistory, timestepsHistory *simulator.CumulativeTimestepsHistory, ) []float64
type PosteriorLogNormalisationIteration ¶
type PosteriorLogNormalisationIteration struct { }
PosteriorLogNormalisationIteration updates the cumulative normalisation of the posterior distribution over params using log-likelihood values given in the state history of other partitions as well as a specified past discounting factor.
func (*PosteriorLogNormalisationIteration) Configure ¶
func (p *PosteriorLogNormalisationIteration) Configure( partitionIndex int, settings *simulator.Settings, )
func (*PosteriorLogNormalisationIteration) Iterate ¶
func (p *PosteriorLogNormalisationIteration) Iterate( params *simulator.OtherParams, partitionIndex int, stateHistories []*simulator.StateHistory, timestepsHistory *simulator.CumulativeTimestepsHistory, ) []float64
type PosteriorMeanIteration ¶
type PosteriorMeanIteration struct { }
PosteriorMeanIteration updates an estimate of the mean of the posterior distribution over params using log-likelihood and param values given in the state history of other partitions.
func (*PosteriorMeanIteration) Configure ¶
func (p *PosteriorMeanIteration) Configure( partitionIndex int, settings *simulator.Settings, )
func (*PosteriorMeanIteration) Iterate ¶
func (p *PosteriorMeanIteration) Iterate( params *simulator.OtherParams, partitionIndex int, stateHistories []*simulator.StateHistory, timestepsHistory *simulator.CumulativeTimestepsHistory, ) []float64