Add more `distproxy`s

[cscherrer]

Rather than trying to rebuild all functionality from Distributions.jl, we're first focusing on reimplementing logdensity (logpdf in Distributions), and delegating most other functions to the current Distributions implementations.

So for example, we have

distproxy(d::Normal{(:μ, :σ)}) = Dists.Normal(d.μ, d.σ)

This makes some functions in Distributions.jl available through distproxy.jl:

PROXIES = Dict(
    :Distributions => [
        :mean
        :std
        :var
        :entropy
        :cdf
        :quantile
        ],
    :MonteCarloMeasurements => [
        :Particles
    ]
)

for m in keys(PROXIES)
    for f in PROXIES[m]
        @eval begin
            import $m: $f
            export $f
            $m.$f(d::AbstractMeasure, args...) = $m.$f(MeasureTheory.distproxy(d), args...)
        end
    end
end

So for example, without ever defining cdf explicitly, we get

julia> Dists.cdf(Normal(2,5),3.1)
0.5870644226482147

julia> @which Dists.cdf(Normal(2,5),3.1)
cdf(d::AbstractMeasure, args...) in MeasureTheory at /home/chad/git/MeasureTheory.jl/src/distproxy.jl:21

We need a distproxy for every parameterization. For example, we should have something like

distproxy(d::Normal{(:μ, :logσ)}) = Dists.Normal(d.μ, exp(d.logσ))

[gdalle]

See #145