Hirrolot

trait Interp {
    type Repr<T>;

    fn lit(i: i32) -> Self::Repr<i32>;
    fn add(a: Self::Repr<i32>, b: Self::Repr<i32>) -> Self::Repr<i32>;
}

struct Eval;

impl Interp for Eval {

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

cscherrer

JuliaActuary/MortalityTables.jl
JuliaActuary/DynamicPolynomials.jl
JuliaActuary/FixedPolynomials.jl
JuliaActuary/MultivariateBases.jl
JuliaActuary/MultivariateMoments.jl
JuliaActuary/MultivariatePolynomials.jl
JuliaActuary/SemialgebraicSets.jl
JuliaActuary/StaticPolynomials.jl
JuliaActuary/TypedPolynomials.jl

mschauer

struct StoProMeasure
    θ::Float64
end

struct WienerMeasure
end

function logdensity(P::StoProMeasure, tr, ::WienerMeasure)
    som = 0.0
    t, x = tr

cscherrer

import PyCall,SymPy
using MLStyle

stats = PyCall.pyimport_conda("sympy.stats", "sympy");
SymPy.import_from(stats)

sym(x) = SymPy.symbols(x);

macro ℓ(expr)
    args = @match expr begin

jiahao

using Dates: now
using DataFrames

import Base: *, push!
mutable struct AAUpdate{Tu,Tv} # Matrix-free representation of the H matrix
    m::Int #:: Size of the AA subspace
    u::Vector{Tu} #:: The quantities s-Hỹ (note typo in paper)
    v::Vector{Tv} #:: The quantities (H'ŝ)/(ŝ'Hŷ)
end

mschauer

function sample_poisson(T, λ, λmax)
	    
	    t = 0.0
	    tt = zeros(0)
	    while t <= T
	        t = t - log(rand())/λmax
	        if rand() ≤ λ(t)/λmax
	            push!(tt, t)
	        end
	    end

mschauer

using LightGraphs
using LightGraphs.SimpleGraphs: fadj
using BenchmarkTools
using Test
macro twice(body)
   quote $(esc(body));$(esc(body)) end
end

struct Done
end

mschauer

using Distributions

K = 10^5
μ1 = 1.0
μ2 = -1.0
σ1 = σ2 = 0.3
args = (μ1 = μ1, σ1 = σ1, μ2 = μ2, σ2 = σ2)

lp(x, args) = log(pdf(Normal(args.μ1, args.σ2), x) + pdf(Normal(args.μ2, args.σ2), x))