Bug Fixes and Easier Generative Models

• Fixes a CmdStan overflow issue in TuringBenchmarks (#662)
• Improved testing coverage for distributions (#621)
• Added an upper bound on step size for HMC-based samplers (#659, #671)
• Fixed an index parsing bug in the Chain type (#658)
• Fixed IPMCMC sampler bug (#663)
• Fixed automatic differentiation through nbinomlogpdf (#664)
• Improved the API on the backend for HMC samplers (#671)
• It's easier to draw from a prior now, so treating an existing Turing model as a generative one no longer requires a long list of default values in the model call (#644, #651). By passing in a Vector{Missing} object, the sampler will draw samples from the prior rather than the posterior. As an example:

# Import packages.
using Turing

# Define a simple Normal model with unknown mean and variance.
@model gdemo(x) = begin
    s ~ InverseGamma(2,3)
    m ~ Normal(0, sqrt(s))
    for i in eachindex(x)
        x[i] ~ Normal(m, sqrt(s))
    end
end

# Treat x as a vector of missing values.
model = gdemo(fill(missing, 2))
c = sample(model, HMC(500, 0.01, 5))