Given a collection of Gaussian distributions $$\mathcal{N}(\mu_i, \sigma_i^2)$$ for $$i=1,\ldots,n$$, compute the Wasserstein barycenter of order 2. For the barycenter computation of variance components, we use a fixed-point algorithm by Álvarez-Esteban et al. (2016) .

Usage

gaussbary1d(means, vars, weights = NULL, ...)

Arguments

means

a length-$$n$$ vector of mean parameters.

vars

a length-$$n$$ vector of variance parameters.

weights

a weight of each image; if NULL (default), uniform weight is set. Otherwise, it should be a length-$$n$$ vector of nonnegative weights.

...

extra parameters including

abstol

stopping criterion for iterations (default: 1e-8).

maxiter

maximum number of iterations (default: 496).

Value

a named list containing

mean

mean of the estimated barycenter distribution.

var

variance of the estimated barycenter distribution.

References

Álvarez-Esteban PC, del Barrio E, Cuesta-Albertos JA, Matrán C (2016). “A Fixed-Point Approach to Barycenters in Wasserstein Space.” Journal of Mathematical Analysis and Applications, 441(2), 744--762. ISSN 0022247X.

gaussbarypd() for multivariate case.

Examples

#----------------------------------------------------------------------
#                         Two Gaussians
#
# Two Gaussian distributions are parametrized as follows.
# Type 1 : (mean, var) = (-4, 1/4)
# Type 2 : (mean, var) = (+4, 1/4)
#----------------------------------------------------------------------
# GENERATE PARAMETERS
par_mean = c(-4, 4)
par_vars = c(0.25, 0.25)

# COMPUTE THE BARYCENTER OF EQUAL WEIGHTS
gmean = gaussbary1d(par_mean, par_vars)

# QUANTITIES FOR PLOTTING
x_grid  = seq(from=-6, to=6, length.out=200)
y_dist1 = stats::dnorm(x_grid, mean=-4, sd=0.5)
y_dist2 = stats::dnorm(x_grid, mean=+4, sd=0.5)
y_gmean = stats::dnorm(x_grid, mean=gmean$mean, sd=sqrt(gmean$var))

# VISUALIZE