torfjelde

julia> using DynamicPPL

julia> @model function demo1(x)
           x ~ Normal()
       end
demo1 (generic function with 1 method)

julia> @model function demo2(x)
           x ~ Normal()
       end

torfjelde

julia> using DynamicPPL, Distributions, Bijectors

julia> using Symbolics

julia> using Symbolics: SymbolicUtils

julia> import StatsFuns

julia> # Make stuff from Distributions.jl primitives so that we don't recurse into them.
       @register Normal()

torfjelde

julia> using Distributions, ForwardDiff

julia> import Distributions: StatsFuns # StatsFuns.jl is used within Distributions.jl, so we just grab from there

julia> ForwardDiff.derivative(λ -> StatsFuns.poiscdf(λ, 1), 1.0)
ERROR: MethodError: no method matching poiscdf(::ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2", Float64}, Float64, 1}, ::Int64)
Closest candidates are:
  poiscdf(::Union{Float64, Int64}, ::Union{Float64, Int64}) at /home/tor/.julia/packages/StatsFuns/zJ1EI/src/rmath.jl:77
Stacktrace:
 [1] (::var"#1#2")(λ::ForwardDiff.Dual{ForwardDiff.Tag{var"#1#2", Float64}, Float64, 1})

torfjelde

julia> using Turing, Random

julia> @model function gdemo(xs)
           # Assumptions
           σ² ~ InverseGamma(2, 3)
           μ ~ Normal(0, √σ²)
           # Observations
           for i = 1:length(xs)
               xs[i] ~ Normal(μ, √σ²)
           end

torfjelde

torfjelde

julia> using Turing

julia> include("utils.jl")

julia> @model function demo(xs)
           s ~ InverseGamma(2, 3)
           m ~ Normal(0, √s)
           for i in eachindex(xs)
               xs[i] ~ Normal(m, √s)
           end

torfjelde

julia> using Bijectors

julia> using ForwardDiff

julia> using ComponentArrays, UnPack

julia> using Bijectors: LeakyReLU, Shift, Scale

julia> # Affine transformation is simply a `Scale` composed with `Shift`
       using Bijectors: Shift, Scale

torfjelde

julia> using ArviZ, Turing

julia> J = 8;

julia> y = [28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0];

julia> σ = [15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0];

julia> schools = [
           "Choate",

torfjelde

using Turing
using TensorBoardLogger, Logging
using OnlineStats      # used to compute different statistics on-the-fly
using StatsBase        # Provides us with the `Histogram` which is supported by `TensorBoardLogger.jl`
using LinearAlgebra 
using DataStructures   # will use a `CircularBuffer` to only keep track of some `n` last samples

struct TBCallback
    logger::TBLogger
end

torfjelde

julia> using Random

julia> using Turing

julia> rng = MersenneTwister(42);

julia> @model function demo(x)
           s ~ InverseGamma(2, 3)
           m ~ Normal(0, √s)
           for i in eachindex(x)