LinearCovarianceModels

LinearCovarianceModels.jl is a package for computing Maximum Likelihood degrees and MLEs of linear covariance models using numerical nonlinear algebra. In particular HomotopyContinuation.jl.

Installation

In order to use LinearCovarianceModels.jl you need to have at least Julia 1.1 installed. If this is not the case you can download it at julialang.org. Please see the platform specific instructions if you have trouble installing Julia.

The package can be installed by executing

julia> using Pkg; Pkg.add("LinearCovarianceModels")

in the Julia REPL.

If you are looking for a more IDE like experience take a look at Juno.

Introduction by Example

# load package
julia> using LinearCovarianceModels

# Create a linear covariance model
julia> Σ = toeplitz(3)
3-dimensional LCModel:
 θ₁  θ₂  θ₃
 θ₂  θ₁  θ₂
 θ₃  θ₂  θ₁

# Compute a witness for the ML degree
julia> W = ml_degree_witness(Σ)
 MLDegreeWitness:
 • ML degree → 3
 • model dimension → 3
 • dual → false

# We offer the option to numerically verify the ML Degree
julia> verify(W)
 Compute additional witnesses for completeness...
 Found 10 additional witnesses
 Found 10 additional witnesses
 Compute trace...
 Norm of trace: 2.6521474798326718e-12
 true

# Consider the sample covariance matrix S
julia> S = [4/5 -9/5 -1/25
            -9/5 79/16 25/24
            -1/25 25/24 17/16];

# We use the ML degree witness set W to compute all critical points of the MLE
# problem.
julia> critical_points(W, S)
3-element Array{Tuple{Array{Float64,1},Float64,Symbol},1}:
 ([2.39038, -0.286009, 0.949965], -5.421751313919751, :local_maximum)
 ([2.52783, -0.215929, -1.45229], -5.346601549034418, :global_maximum)
 ([2.28596, -0.256394, 0.422321], -5.424161999175718, :saddle_point)  

# If we are just interested in the MLE, there is also a shorthand.
julia> mle(W, S)
3-element Array{Float64,1}:
  2.527832268219689  
 -0.21592947057775033
 -1.4522862659134732

For more informations take a look at the documentation.