def create_gaussian_prior_for(parameter, mean, std):
    """
	Use for informed variables.
	The Gaussian prior weights by a Gaussian in the parameter. If you 
	would like the logarithm of the parameter to be weighted by
	a Gaussian, create a ancillary parameter (see create_jeffreys_prior_for).
	
	:param parameter: Parameter to create a prior for. E.g. xspowerlaw.mypowerlaw.PhoIndex

	"""
    from . import invgauss
    lo = parameter.min
    hi = parameter.max
    f = invgauss.get_invgauss_func(mean, std)
    return lambda x: max(lo, min(hi, f(x)))
