Distributions to implement

[rezayas]

Continuous distributions

• Uniform
• Exponential (and shifted)
• Normal
• LogNormal (and shifted)
• Gamma (and shifted) Also take mean and StDev as parameters
• Weibull Also take mean and StDev as parameters
• Beta Also take mean and StDev as parameters
• Dirichlet
• Johnson Su https://www.jmp.com/support/help/13/Continuous_Fit_Distributions.shtml
• Johnson Sb https://www.jmp.com/support/help/13/Continuous_Fit_Distributions.shtml
• Johnson SI https://www.jmp.com/support/help/13/Continuous_Fit_Distributions.shtml
• Triangular
• Chi

Discrete distributions

• Bernouli
• Empirical (categorical)
• Discrete uniform
• Poisson
• Binomial
• Multinomial
• Gamma-Poisson
• Beta-Binomial
• Negative Binomial

[NoLongerBreathedIn]

I can't do Weibull mean/stdev, because those are (a) complicated, and (b) require inverse gamma functions.

[NoLongerBreathedIn]

I can't do Dirichlet; that's a multidimensional distribution, and in the K=2 case it's just beta.

[NoLongerBreathedIn]

Multinomial is out too.

[rezayas]

Let's find a way to also implement multi-dimensional distributions. We need them!

[NoLongerBreathedIn]

That's done. Ideally, we could do Dirichlet-multinomial as well, but that's a bit tricky on the pdf front. On the simple generation front, it's a lot easier; just pick a random from Dirichlet and feed to multinomial.