“”"
qsimvnv(Σ,a,b;m=iterations)
Computes the Multivariate Normal probability integral using a quasi-random rule
with m points for positive definite covariance matrix Σ, mean [0,…], with lower
integration limit vector a and upper integration limit vector b.
\\Phi_k(\\mathbf{a},\\mathbf{b},\\mathbf{\\Sigma} ) = \\frac{1}{\\sqrt{\\left | \\mathbf{\\Sigma} \\right |{(2\\pi )^k}}}\\int_{a_1}^{b_1}\\int_{a_2}^{b_2}\\begin{align*}
&...\\end{align*} \\int_{a_k}^{b_k}e^{^{-\\frac{1}{2}}\\mathbf{x}^t{\\mathbf{\\Sigma }}^{-1}\\boldsymbol{\\mathbf{x}}}dx_k...dx_1
Probability p is output with error estimate e.
Arguments
-
Σ::AbstractArray: positive-definite covariance matrix of MVN distribution
-
a::Vector: lower integration limit column vector
-
b::Vector: upper integration limit column vector
-
m::Int64: number of integration points (default 1000*dimension)
Example
julia> r = [4 3 2 1;3 5 -1 1;2 -1 4 2;1 1 2 5]
julia> a=[-Inf; -Inf; -Inf; -Inf]
julia> b = [1; 2; 3; 4 ]
julia> m = 5000
julia> (p,e)=qsimvnv(r,a,b;m=m)
(0.605219554009911, 0.0015718064928452481)
Results will vary slightly from run-to-run due to the quasi-Monte Carlo
algorithm.
Non-central MVN distributions (with non-zero mean) can use this function by adjusting
the integration limits. Subtract the mean vector, μ, from each
integration vector.
Example
julia> #non-central MVN
julia> Σ=[4 2;2 3]
julia> μ = [1;2]
julia> a=[-Inf; -Inf]
julia> b=[2; 2]
julia> (p,e) = qsimvnv(Σ,a-μ,b-μ)
(0.4306346895870772, 0.00015776288569406053)
“”"
function qsimvnv(Σ,a,b;m=nothing)
#= rev 1.13
This function uses an algorithm given in the paper
"Numerical Computation of Multivariate Normal Probabilities", in
J. of Computational and Graphical Stat., 1(1992), pp. 141-149, by
Alan Genz, WSU Math, PO Box 643113, Pullman, WA 99164-3113
Email : alangenz@wsu.edu
The primary references for the numerical integration are
"On a Number-Theoretical Integration Method"
H. Niederreiter, Aequationes Mathematicae, 8(1972), pp. 304-11, and
"Randomization of Number Theoretic Methods for Multiple Integration"
R. Cranley and T.N.L. Patterson, SIAM J Numer Anal, 13(1976), pp. 904-14.
Re-coded in Julia from the MATLAB function qsimvnv(m,r,a,b)
Alan Genz is the author the MATLAB qsimvnv() function.
Alan Genz software website: http://archive.is/jdeRh
Source code to MATLAB qsimvnv() function: http://archive.is/h5L37
% QSIMVNV(m,r,a,b) and _chlrdr(r,a,b)
%
% Copyright (C) 2013, Alan Genz, All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided the following conditions are met:
% 1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% 2. Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the
% distribution.
% 3. The contributor name(s) may not be used to endorse or promote
% products derived from this software without specific prior
% written permission.
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
% "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
% LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
% FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
% COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
% INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
% BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
% OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
% ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
% TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF USE
% OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
%
Julia dependencies
Distributions
PDMats
Primes
Random
LinearAlgebra
=#
if isnothing(m)
m = 1000*size(Σ,1) # default is 1000 * dimension
end
# check for proper dimensions
n=size(Σ,1)
nc=size(Σ,2) # assume square Cov matrix nxn
# check dimension > 1
n >= 2 || throw(ErrorException("dimension of Σ must be 2 or greater. Σ dimension: $(size(Σ))"))
n == nc || throw(DimensionMismatch("Σ matrix must be square. Σ dimension: $(size(Σ))"))
# check dimensions of lower vector, upper vector, and cov matrix match
(n == size(a,1) == size(b,1)) || throw(DimensionMismatch("iconsistent argument dimensions. Sizes: Σ $(size(Σ)) a $(size(a)) b $(size(b))"))
# check that a and b are column vectors; if row vectors, fix it
if size(a,1) < size(a,2)
a = transpose(a)
end
if size(b,1) < size(b,2)
b = transpose(b)
end
# check that lower integration limit a < upper integration limit b for all elements
all(a .<= b) || throw(ArgumentError("lower integration limit a must be <= upper integration limit b"))
# check that Σ is positive definate; if not, print warning
isposdef(Σ) || @warn "covariance matrix Σ fails positive definite check"
# check if Σ, a, or b contains NaNs
if any(isnan.(Σ)) || any(isnan.(a)) || any(isnan.(b))
p = NaN
e = NaN
return (p,e)
end
# check if a==b
if a == b
p = 0.0
e = 0.0
return (p,e)
end
# check if a = -Inf & b = +Inf
if all(a .== -Inf) && all(b .== Inf)
p = 1.0
e = 0.0
return (p,e)
end
# check input Σ, a, b are floats; otherwise, convert them
if eltype(Σ)<:Signed
Σ = float(Σ)
end
if eltype(a)<:Signed
a = float(a)
end
if eltype(b)<:Signed
b = float(b)
end
##################################################################
#
# Special cases: positive Orthant probabilities for 2- and
# 3-dimesional Σ have exact solutions. Integration range [0,∞]
#
##################################################################
if all(a .== zero(eltype(a))) && all(b .== Inf) && n <= 3
Σstd = sqrt.(diag(Σ))
Rcorr = cov2cor(Σ,Σstd)
if n == 2
p = 1/4 + asin(Rcorr[1,2])/(2π)
e = eps()
elseif n == 3
p = 1/8 + (asin(Rcorr[1,2]) + asin(Rcorr[2,3]) + asin(Rcorr[1,3]))/(4π)
e = eps()
end
return (p,e)
end
##################################################################
#
# get lower cholesky matrix and (potentially) re-ordered integration vectors
#
##################################################################
(ch,as,bs)=_chlrdr(Σ,a,b) # ch =lower cholesky; as=lower vec; bs=upper vec
##################################################################
#
# quasi-Monte Carlo integration of MVN integral
#
##################################################################
### setup initial values
ai=as[1]
bi=bs[1]
ct=ch[1,1]
unitnorm = Normal() # unit normal distribution
rng=RandomDevice()
# if ai is -infinity, explicity set c=0
# implicitly, the algorith classifies anythign > 9 std. deviations as infinity
if ai > -9*ct
if ai < 9*ct
c1 = cdf.(unitnorm,ai/ct)
else
c1 = 1.0
end
else
c1 = 0.0
end
# if bi is +infinity, explicity set d=0
if bi > -9*ct
if bi < 9*ct
d1 = cdf(unitnorm,bi/ct)
else
d1 = 1.0
end
else
d1 = 0.0
end
#n=size(Σ,1) # assume square Cov matrix nxn
cxi=c1 # initial cxi; genz uses ci but it conflicts with Lin. Alg. ci variable
dci=d1-cxi # initial dcxi
p=0.0 # probablity = 0
e=0.0 # error = 0
# Richtmyer generators
ps=sqrt.(primes(Int(floor(5*n*log(n+1)/4)))) # Richtmyer generators
q=ps[1:n-1,1]
ns=12
nv=Int(max(floor(m/ns),1))
Jnv = ones(1,nv)
cfill = transpose(fill(cxi,nv)) # evaulate at nv quasirandom points row vec
dpfill = transpose(fill(dci,nv))
y = zeros(n-1,nv) # n-1 rows, nv columns, preset to zero
#=Randomization loop for ns samples
j is the number of samples to integrate over,
but each with a vector nv in length
i is the number of dimensions, or integrals to comptue =#
for j in 1:ns # loop for ns samples
c = copy(cfill)
dc = copy(dpfill)
pv = copy(dpfill)
for i in 2:n
x=transpose(abs.(2.0 .* mod.((1:nv) .* q[i-1] .+ rand(rng),1) .- 1)) # periodizing transformation
# note: the rand() is not broadcast -- it's a single random uniform value added to all elements
y[i-1,:] = quantile.(unitnorm,c .+ x.*dc)
s = transpose(ch[i,1:i-1]) * y[1:i-1,:]
ct=ch[i,i] # ch is cholesky matrix
ai=as[i] .- s
bi=bs[i] .- s
c=copy(Jnv) # preset to 1 (>9 sd, +∞)
d=copy(Jnv) # preset to 1 (>9 sd, +∞)
c[findall(x -> isless(x,-9*ct),ai)] .= 0.0 # If < -9 sd (-∞), set to zero
d[findall(x -> isless(x,-9*ct),bi)] .= 0.0 # if < -9 sd (-∞), set to zero
tstl = findall(x -> isless(abs(x),9*ct),ai) # find cols inbetween -9 and +9 sd (-∞ to +∞)
c[tstl] .= cdf.(unitnorm,ai[tstl]/ct) # for those, compute Normal CDF
tstl = (findall(x -> isless(abs(x),9*ct),bi)) # find cols inbetween -9 and +9 sd (-∞ to +∞)
d[tstl] .= cdf.(unitnorm,bi[tstl]/ct)
@. dc=d-c
@. pv=pv * dc
end # for i=
d=(mean(pv)-p)/j
p += d
e=(j-2)*e/j+d^2
end # for j=
e=3*sqrt(e) # error estimate is 3 times standard error with ns samples
return (p,e) # return probability value and error estimate
end # function qsimvnv
“”"
Computes permuted lower Cholesky factor c for R which may be singular,
also permuting integration limit vectors a and b.
Arguments
r matrix Matrix for which to compute lower Cholesky matrix
when called by mvn_cdf(), this is a covariance matrix
a vector column vector for the lower integration limit
algorithm may permutate this vector to improve integration
accuracy for mvn_cdf()
b vector column vector for the upper integration limit
algorithm may pertmutate this vector to improve integration
accuracy for mvn_cdf()
Output
tuple An a tuple with 3 returned arrays:
1 - lower Cholesky root of r
2 - lower integration limit (perhaps permutated)
3 - upper integration limit (perhaps permutated)
Examples
r = [1 0.25 0.2; 0.25 1 0.333333333; 0.2 0.333333333 1]
a = [-1; -4; -2]
b = [1; 4; 2]
(c, ap, bp) = _chlrdr(r,a,b)
result:
Lower cholesky root:
c = [ 1.00 0.0000 0.0000,
0.20 0.9798 0.0000,
0.25 0.2892 0.9241 ]
Permutated upper input vector:
ap = [-1, -2, -4]
Permutated lower input vector:
bp = [1, 2, 4]
Related Functions
mvn_cdf - multivariate Normal CDF function makes use of this function
“”"
function _chlrdr(Σ,a,b)
# Rev 1.13
# define constants
# 64 bit machien error 1.0842021724855e-19 ???
# 32 bit machine error 2.220446049250313e-16 ???
ep = 1e-10 # singularity tolerance
if Sys.WORD_SIZE == 64
fpsize=Float64
ϵ = eps(0.0) # 64-bit machine error
else
fpsize=Float32
ϵ = eps(0.0f0) # 32-bit machine error
end
if !@isdefined sqrt2π
sqrt2π = √(2π)
end
# unit normal distribution
unitnorm = Normal()
n = size(Σ,1) # covariance matrix n x n square
ckk = 0.0
dem = 0.0
am = 0.0
bm = 0.0
ik = 0.0
if eltype(Σ)<:Signed
c = copy(float(Σ))
else
c = copy(Σ)
end
if eltype(a)<:Signed
ap = copy(float(a))
else
ap = copy(a)
end
if eltype(b)<:Signed
bp = copy(float(b))
else
bp = copy(b)
end
d=sqrt.(diag(c))
for i in 1:n
if d[i] > 0.0
c[:,i] /= d[i]
c[i,:] /= d[i]
ap[i]=ap[i]/d[i] # ap n x 1 vector
bp[i]=bp[i]/d[i] # bp n x 1 vector
end
end
y=zeros(fpsize,n) # n x 1 zero vector to start
for k in 1:n
ik = k
ckk = 0.0
dem = 1.0
s = 0.0
#pprinta(c)
for i in k:n
if c[i,i] > ϵ # machine error
cii = sqrt(max(c[i,i],0))
if i>1 && k>1
s=(c[i,1:(k-1)].*y[1:(k-1)])[1]
end
ai=(ap[i]-s)/cii
bi=(bp[i]-s)/cii
de = cdf(unitnorm,bi) - cdf(unitnorm,ai)
if de <= dem
ckk = cii
dem = de
am = ai
bm = bi
ik = i
end
end # if c[i,i]> ϵ
end # for i=
i = n
if ik>k
ap[ik] , ap[k] = ap[k] , ap[ik]
bp[ik] , bp[k] = bp[k] , bp[ik]
c[ik,ik] = c[k,k]
if k > 1
c[ik,1:(k-1)] , c[k,1:(k-1)] = c[k,1:(k-1)] , c[ik,1:(k-1)]
end
if ik<n
c[(ik+1):n,ik] , c[(ik+1):n,k] = c[(ik+1):n,k] , c[(ik+1):n,ik]
end
if k<=(n-1) && ik<=n
c[(k+1):(ik-1),k] , c[ik,(k+1):(ik-1)] = transpose(c[ik,(k+1):(ik-1)]) , transpose(c[(k+1):(ik-1),k])
end
end # if ik>k
if ckk > k*ep
c[k,k]=ckk
if k < n
c[k:k,(k+1):n] .= 0.0
end
for i in (k+1):n
c[i,k] /= ckk
c[i:i,(k+1):i] -= c[i,k]*transpose(c[(k+1):i,k])
end
if abs(dem)>ep
y[k] = (exp(-am^2/2)-exp(-bm^2/2))/(sqrt2π*dem)
else
if am<-10
y[k] = bm
elseif bm>10
y[k]=am
else
y[k]=(am+bm)/2
end
end # if abs
else
c[k:n,k] .== 0.0
y[k] = 0.0
end # if ckk>ep*k
end # for k=
return (c, ap, bp)
end # function _chlrdr
function testmvn(;m=nothing)
#Typical Usage/Example Code
#Example multivariate Normal CDF for various vectors
println()
# from MATLAB documentation
r = [4 3 2 1;3 5 -1 1;2 -1 4 2;1 1 2 5]
a=[-Inf; -Inf; -Inf; -Inf] # -inf for each
b = [1; 2; 3; 4 ]
m = 5000
#m=4000 # rule of thumb: 1000*(number of variables)
(myp,mye)=qsimvnv(r,a,b;m=m)
println("Answer should about 0.6044 to 0.6062")
println(myp)
println("The Error should be <= 0.001 - 0.0014");
println(mye)
r=[1 0.6 0.333333;
0.6 1 0.733333;
0.333333 0.733333 1]
r = [ 1 3/5 1/3;
3/5 1 11/15;
1/3 11/15 1]
a=[-Inf;-Inf;-Inf]
b=[1;4;2]
#m=3000;
(myp,mye)=qsimvnv(r,a,b;m=4000)
println()
println("Answer shoudl be about 0.82798")
# answer from Genz paper 0.827984897456834
println(myp)
println("The Error should be <= 2.5-05")
println(mye)
r=[1 0.25 0.2; 0.25 1 0.333333333; 0.2 0.333333333 1]
a=[-1;-4;-2]
b=[1;4;2]
#m=3000;
(myp,mye)=qsimvnv(r,a,b;m=4000);
println()
println("Answer should be about 0.6537")
println(myp)
println("The Error should be <= 2.5-05")
println(mye)
# Genz problem 1.5 p. 4-5 & p. 63
# correct answer F(a,b) = 0.82798
r = [1/3 3/5 1/3;
3/5 1.0 11/15;
1/3 11/15 1.0]
a = [-Inf; -Inf; -Inf]
b = [1; 4; 2]
(myp,mye)=qsimvnv(r,a,b;m=4000)
println()
#println("Answer shoudl be about 0.82798")
println("Answer should be 0.9432")
println(myp)
println("The error should be < 6e-05")
println(mye)
# Genz page 63 uses a different r Matrix
r = [1 0 0;
3/5 1 0;
1/3 11/15 1]
a = [-Inf; -Inf; -Inf]
b = [1; 4; 2]
(myp,mye)=qsimvnv(r,a,b;m=4000)
println()
println("Answer shoudl be about 0.82798")
println(myp)
println("The error should be < 6e-05")
println(mye)
# mystery solved - he used the wrong sigma Matrix
# when computing the results on page 6
# singular cov Example
r = [1 1 1; 1 1 1; 1 1 1]
a = [-Inf, -Inf, -Inf]
b = [1, 1, 1]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be 0.841344746068543")
println(myp)
println("The error should be 0.0")
println(mye)
println("Cov matrix is singular")
println("Problem reduces to a univariate problem with")
println("p = cdf.(Normal(),1) = ",cdf.(Normal(),1))
# 5 dimensional Example
#c = LinearAlgebra.tri(5)
r = [1 1 1 1 1;
1 2 2 2 2;
1 2 3 3 3;
1 2 3 4 4;
1 2 3 4 5]
a = [-1,-2,-3,-4,-5]
b = [2,3,4,5,6]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.7613")
# genz gives 0.4741284 p. 5 of book
# Julia, MATLAB, and R all give ~0.7613 !
println(myp)
println("The error should be < 0.001")
println(mye)
# genz reversed the integration limits when computing
# see p. 63
a = sort!(a)
b = 1 .- a
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.4741284")
# genz gives 0.4741284 p. 5 of book
# now matches with reversed integration limits
println(myp)
println("The error should be < 0.001")
println(mye)
# positive orthant of above
a = [0,0,0,0,0]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.11353418")
# genz gives 0.11353418 p. 6 of book
println(myp)
println("The error should be < 0.001")
println(mye)
# now with a = -inf
a = [-Inf,-Inf,-Inf,-Inf,-Inf]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.81031455")
# genz gives 0.81031455 p. 6 of book
println(myp)
println("The error should be < 0.001")
println(mye)
# eight dimensional Example
r = [1 1 1 1 1 1 1 1;
1 2 2 2 2 2 2 2;
1 2 3 3 3 3 3 3;
1 2 3 4 4 4 4 4;
1 2 3 4 5 5 5 5;
1 2 3 4 5 6 6 6;
1 2 3 4 5 6 7 7;
1 2 3 4 5 6 7 8]
a = -1*[1,2,3,4,5,6,7,8]
b = [2,3,4,5,6,7,8,9]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.7594")
# genz gives 0.32395 p. 6 of book
# MATLAB gives 0.7594362
println(myp)
println("The error should be < 0.001")
println(mye)
# eight dim orthant
a=[0,0,0,0,0,0,0,0]
b=[Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.196396")
# genz gives 0.076586 p. 6 of book
# MATLB gives 0.196383
println(myp)
println("The error should be < 0.001")
println(mye)
# eight dim with a = -inf
a=-Inf*[1,1,1,1,1,1,1,1]
b = [2,3,4,5,6,7,8,9]
#b = [0,0,0,0,0,0,0,0]
(myp,mye)=qsimvnv(r,a,b)
println()
println("Answer should be ~ 0.9587")
# genz gives 0.69675 p. 6 of book
# MATLAB gives 0.9587
println(myp)
println("The error should be < 0.001")
println(mye)
end # testmvn
).