Normal-scaled inverse gamma distribution
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Probability density function |
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Cumulative distribution function |
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Parameters | location (real) (real) (real) (real) |
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Probability density function (pdf) | |
Cumulative distribution function (cdf) | |
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Characteristic function |
In probability theory and statistics, the normal-scaled inverse gamma distribution is a four-parameter family of multivariate continuous probability distributions. It is the conjugate prior of a normal distribution with unknown mean and variance.
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[edit] Definition
Suppose
has a normal distribution with mean ν and variance σ2, where
has an inverse gamma distribution. Then (μ,σ2) has a normal-scaled inverse gamma distribution, denoted as
[edit] Characterization
[edit] Probability density function
[edit] Alternative Parameterization
It is also possible to let γ = 1 / λ in which case the pdf becomes
[edit] Cumulative distribution function
[edit] Properties
[edit] Summation
[edit] Scaling
[edit] Exponential family
[edit] Information entropy
[edit] Kullback-Leibler divergence
[edit] Maximum likelihood estimation
[edit] Generating normal-gamma random variates
Generation of random variates is straightforward:
- Sample σ2 from an inverse gamma distribution with parameters α and β
- Sample μ from a normal distribution with mean ν and variance σ2 / λ
[edit] Related distributions
- is normally distributed
- is distributed as an inverse gamma
- The normal-gamma distribution is the same distribution parameterized by precision rather than variance
- A generalization of this distribution which allows for a multivariate mean and a positive-definite covariance matrix is the Multivariate normal-inverse Wishart distribution
[edit] References
Dominici, Francesca; Giovanni Parmigiani, Merlise Clyde (2000-09). "Conjugate Analysis of Multivariate Normal Data with Incomplete Observations". The Canadian Journal of Statistics / La Revue Canadienne de Statistique 28 (3): 533-550. ISSN 03195724.