mschauer

[deps]
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
SampleChainsDynamicHMC = "6d9fd711-e8b2-4778-9c70-c1dfb499d4c4"
Soss = "8ce77f84-9b61-11e8-39ff-d17a774bf41c"
TupleVectors = "615932cf-77b6-4358-adcd-5b7eba981d7e"

mschauer

module Adamopt

# This is a module implementing vanilla Adam (https://arxiv.org/abs/1412.6980).
export Adam, step!

# Struct containing all necessary info
mutable struct Adam
  theta::AbstractArray{Float64} # Parameter array
  loss::Function                # Loss function
  grad::Function                # Gradient function

mschauer

using Distributions

# Search counter examples in exact arithmetic

xpdf(d::Binomial,k) = binomial(d.n, k)*d.p^k*(1 - d.p)^(d.n - k)

f(x,y) = x*y + (1-x)*(1-y)
   
while true

mschauer

using Random, LinearAlgebra, Distributions, StatsBase
# Linear Algebra of Markov chain

# Example: Weather Markov chain of Oz
S = [:R, :S, :C]
P = [0.5 0.25 0.25
     0.5  0    0.5
     0.25 0.25 0.5]

sum(P[2, :])

mschauer

using Observables, BenchmarkTools, Test

struct StaticObservable{T, Listeners} <: Observables.AbstractObservable{T}
    listeners::Listeners
    val::Base.RefValue{T}
end

StaticObservable(obs::Observable) = StaticObservable(tuple(obs.listeners...), Ref(obs[]))
function StaticObservable(val::Ref, listeners...) 
    _notify(val, listeners)

mschauer

n = 1000
x = range(0, 1, length=n)
ς = 1.5 # noise level

μ = 3*x.*sin.(2pi*x) # periodic signal in time domain
#μ = 6*sqrt.(abs.(x .- 0.5)) # this one is difficult to estimate

# Model: Signal distorted by white noise
y = μ + ς*randn(n)  

mschauer

using Mitosis
using MitosisStochasticDiffEq
import MitosisStochasticDiffEq as MSDE
using StaticArrays, LinearAlgebra
using OrdinaryDiffEq

# Match with B and sigma
B(θ) = [-0.1 0.2θ; -0.2θ -0.1]
beta(θ) = [0.,0.]
Σ(θ) = 0.15*I(2)

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)


"""