mschauer

using AdvancedMH
using Distributions
using Random
using MCMCChains
using StructArrays
struct MvWiener{Tu0,Ttrange} <: ContinuousMatrixDistribution
    u0::Tu0
    trange::Ttrange
end

mschauer

using LinearAlgebra, Test, Random, Statistics
outer(x) = x*x'
lchol(x) = cholesky(Symmetric(x)).L
Random.seed!(1)

# dimensions
n = 10 # observed
p = 5 # latent

# parameters

mschauer

using StaticArrays
struct EulerMaruyama!
end


"""
    tangent!(du, u, dz, P)


"""

mschauer

  using GitHub, JSON
  
  #myauth = GitHub.authenticate(ENV["GITHUB_AUTH"])
  
  gist_json(fn; description="$fn", public=true) = JSON.parse(
  """
  {
    "description": "$description",
    "public": $public,
    "files": {

mschauer

function mappairreduce(r, f, itr, dec)
    ϕ  = iterate(itr)
    if ϕ === nothing
        return dec
    end
    xlast, state = ϕ
    while true
        ϕ = iterate(itr, state)
        if ϕ === nothing 
            return dec

mschauer

using Makie
using Colors
n = 100
f(x) = 0.5 .*reim(((x[1] + x[2]*im)/33)^2)
function f2(x) 
    0.5.*abs(2*(x[1] + x[2]*im)/1089)
end
#f(x) = (x[1],x[2])
#f2(x) = 1.0

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