mschauer

julia> 

shell> cd example/phylo3/
/Users/smoritz/.julia/dev/MitosisStochasticDiffEq/example/phylo3

julia> include("phylo_autobffg3.jl")
  Activating environment at `~/.julia/dev/MitosisStochasticDiffEq/example/phylo3/Project.toml`
Internal error: encountered unexpected error in runtime:
BoundsError(a=Array{Int64, (30,)}[0, 0, 2, 2, 2, 1, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 0, 0, 0, 1, 0, 0, 0], i=(66,))
jl_bounds_error_ints at /Applications/Julia-1.6.app/Contents/Resources/julia/lib/julia/libjulia-internal.1.6.dylib (unknown line)

mschauer

using ZigZagBoomerang
using SparseArrays
using ForwardDiff
using ForwardDiff: Dual, value, partials
const D1 = Dual{Nothing, Float64, 1}
const D𝕏 = typeof(D1.(zero(𝕏)))
function partiali(F, z, i)
    z[i] = Dual(z[i], 1.0)
    r = partials(F(z))[]
    z[i] = Dual(z[i], 0.0)

mschauer

d = 100
ts_ = range(0, 1, length=d+1)[2:end]
Γ = SymTridiagonal([2.0ones(d-1); 1.0], -ones(d-1))/ts_[1]

# Note the covariance matrix of B.M is 
ts = range(0, 1, length=d+1)[2:end]
Γ0 = inv([min(i,j) for i in ts_, j in ts_])
@test norm(Γ0 - Γ) < 1e-7

mschauer

using ZigZagBoomerang
using StaticArrays
using LinearAlgebra
using SparseArrays
using Random
using Test
using Statistics
Random.seed!(2)
using StaticArrays

mschauer

using Random, LinearAlgebra, Distributions, StatsBase
using StaticArrays

# use representation x*exp(l) to compute the likelihood without underflow
struct Exp{T} <: Number 
    x::T
    l::T
end

Exp(x::T) where {T<:Real} = Exp(x, zero(x))

mschauer

using Soss: logdensity, xform, ConditionalModel
using ZigZagBoomerang
using ForwardDiff
using ForwardDiff: gradient!
using LinearAlgebra
using SparseArrays
using StructArrays
using TransformVariables

mschauer

xnew = sample(A[x, :]) 

struct TransitionKernel
   A::Matrix 
end
(kappa::TransitionKernel)(x) = kappa.A[x, :]

A = [0.5 0.5 0
     0 0.3 0.7

mschauer

export basemeasure

import MeasureTheory

function MeasureTheory.basemeasure(c::ConditionalModel{A,B,M}, x=NamedTuple()) where {A,B,M}
    _basemeasure(M, Model(c), argvals(c), observations(c), x)
end

export sourceBasemeasure

mschauer

using Mitosis
using MeasureTheory
using Random
using Test

n = 30
ρ = rand(n)

struct PlusBernoulli{S,T} <: MeasureTheory.AbstractMeasure
    x::S

mschauer

using ZigZagBoomerang
using Distributions
using SparseArrays
using LinearAlgebra
using Random
Random.seed!(1)
# Million data points, 5 parameters + intercept
N = 1000000
k = 5;
X = randn(N,k);