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);

mschauer

using IterativeSolvers
using LinearAlgebra

A = rand(-10:10, 16, 16) + 5*LinearAlgebra.I
x = rand(-10:10, 16)
b = A*x


y = idrs(BigFloat.(A), BigFloat.(b), s=4, tol=1e-100, maxiter=100, verbose=true)

mschauer

using Distributions, Statistics, LinearAlgebra

## simulate sample trajectory

μ = [1.0, 0.0]
Σ = [0.5  0.0
     0.0  0.5]
x0 = rand(MultivariateNormal(μ, Σ))   #start point

β = [0.0, 0.0]

mschauer

# Fit a Brownian motion with drift to and scale to
# Georgia election data:
# fraction of total count of votes vs difference in votes

data = [0.9222672139181572 103896.10389610389
    0.9267802009311443 87662.33766233765
    0.9277217593727027 85714.28571428571
    0.9285683655966674 83116.88311688312
    0.9299797843665768 79220.77922077922
    0.9310169076206811 78571.42857142857

mschauer

#=
Newborn babies are more likely to be boys than girls.
A random sample found 13 173 boys were born among 25 468
newborn children. Population: All children born that time period. Alternatively,
the unknown p could be the unknown probability.
=#

b = 13173
n = 25486