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David Widmann devmotion

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I may be slow to respond.
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View debug.txt
julia> using DynamicPPL
julia> @model function demo4(n, ::Type{TV}=Vector{Float64}) where {TV}
m ~ Normal()
x = TV(undef, n)
@show __varinfo__
for i in eachindex(x)
x[i] ~ Normal(m, 1.0)
end
end
View discrete1d_benchmarks.jl
using BenchmarkTools
using Distances
using Distributions
using StatsBase
using LinearAlgebra
using SparseArrays
function f(
c, μ::DiscreteNonParametric, ν::DiscreteNonParametric, plan::SparseMatrixCSC
View ot.jl
using Distributions
using SparseArrays
using LinearAlgebra
using StatsBase
function _ot_cost_plan(c, μ::DiscreteNonParametric, ν::DiscreteNonParametric; get=:plan)
len_μ = length.p)
len_ν = length.p)
wi = μ.p[1]
wj = ν.p[1]
View sde.jl
using AdvancedMH
using ArraysOfArrays
using CairoMakie
using DiffEqNoiseProcess
using Distributions
using StochasticDiffEq
using Turing
using Random
struct CrankNicolsonProposal{P,T} <: AdvancedMH.Proposal{P}
View emcee.ipynb
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View processes.jl
using Turing
using Turing.RandomMeasures
using Random
function stickbreaking(rpm = DirichletProcess(0.25))
# Data
data = [-2,2,-1.5,1.5]
# Base distribution
@devmotion
devmotion / examples.jl
Last active Dec 19, 2019
ESS examples
View examples.jl
using Turing
using StatsPlots
using Random
using Statistics
function demo(N::Int; n::Int = 10)
# observation noise
σ² = 0.3
@devmotion
devmotion / demo.jl
Last active Nov 24, 2019
Elliptical slice sampling
View demo.jl
using Turing
using StatsPlots
using Random
using Statistics
function demo(N::Int; n::Int = 10)
# observation noise
σ² = 0.3
@devmotion
devmotion / blackboxoptim.jl
Last active Jul 31, 2019
DDE parameter estimation
View blackboxoptim.jl
using DelayDiffEq, DiffEqParamEstim, BlackBoxOptim, DataFrames, LsqFit
using Plots
gr()
include("importData.jl")
include("plot.jl")
# import data from the path, in which:
# pop: population data
# g1, g2: g1 and g2 data
@devmotion
devmotion / benchmark.jl
Created Aug 5, 2018
Interpolation benchmarks
View benchmark.jl
using MuladdMacro, EllipsisNotation, BenchmarkTools
# In place
@generated function f_loop!(out, dt, y, k, b, idxs::Nothing, ::Type{Val{N}}) where N
sumexpr = :(+($((:(k[$i][idx]*b[$i]) for i in 1:N)...)))
quote
@muladd @inbounds for idx in eachindex(out)
out[idx] = y[idx] + dt*$sumexpr
end