Comments (2)
I think something like this would be sufficient:
_quad(d::AbstractMvNormal, t::AbstractVector) = dot(t, cov(d), t)
_quad(d::MvNormal, t::AbstractVector) = PDMats.quad(cov(d), t)
_quad(d::MvNormalCanon, t::AbstractVector) = PDMats.invquad(d.J, t)
function cf(d::AbstractMvNormal, t::AbstractVector)
return exp(im * dot(mean(d), t) - _quad(d, t) / 2)
end
function mgf(d::AbstractMvNormal, t::AbstractVector)
return exp(dot(mean(d), t) + _quad(d, t) / 2)
end
from distributions.jl.
Thanks! This looks simple and good.
from distributions.jl.
Related Issues (20)
- Changing the type from `Float` to `Int` breaks `mean` of `MixtureModel`
- `0 <= cdf(Normal(),x) <= 1` not always true. HOT 4
- `0 <= cdf(truncated(Normal(2.5, 0.2), lower=0.0),x)` not always true. HOT 3
- Suggestion: 3-argument cdf() HOT 1
- Error in xval in gumbel
- New GPL dependency introduced through AliasTables HOT 5
- JET reports type-instability when sampling from `Beta` HOT 8
- Unexpected behavior of `truncated` for zero variance distributions at the lower bound HOT 2
- Type instability of mixture distributions
- Bug in quantile() for some special cases of a MixtureModel
- Rename `ValueSupport` subtypes to more specific names HOT 1
- Who is maintaining Distributions.jl? HOT 3
- cdf of InverseGaussian HOT 1
- Support parametrization of Beta (Mean and variance Beta(μ, σ²)) HOT 1
- `Distributions.jl` and `RecursiveArrayTools.jl` do not work together.
- Degenerate Distribution interface is probably necessary HOT 1
- Better samplers for Bernoulli(p) and Geometric(1/2) random variables HOT 2
- Is there a reason `rand(TDist(nu))` does not obey the type of `nu`? HOT 10
- How to draw a density sample from a multivariate normal HOT 1
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from distributions.jl.