A method of optimizing parameters in a hearing aid system comprising the steps of: - providing a set of parameters x 1, x 2,..., to be optimized; - providing a first sound based on a first parameter value setting x 1 = [x 11, x 21,.., x d1] and providing a second sound based on a second parameter value setting x 2 = [x 12, x 22,..., xd 2]; - prompting a user to rate said first and second sounds relative to each other; - providing a user response y 1 representing the user's rating of the two sounds relative to each other; - providing m user responses y = [y 1, y 2,..., y m] wherein each of the user responses represent the user's rating of two sounds relative to each other and wherein the sounds are derived from a multitude of n parameter value settings x 1, x 2,..., x n; - defining a Gaussian likelihood function as: p y k | f x u, f x v, σ 2 = N z k | f x u − f x v, σ 2 wherein f ( x u) and f ( x v) represent specific function values of the user's internal response function f, wherein y k represents a specific user response, wherein σ 2 represents the variance of the user response and wherein N y k | f x u − f x v, σ 2 is the single variate Gaussian distribution over the variable y k with mean value f ( x u) - f ( x v) and variance σ 2; - defining the likelihood as: p y | f = ∏ k = 1 m N y k | f x u − f x v, σ 2 = N y | Mf, σ 2 I wherein N y | Mf, σ 2 I m × m represents a multivariate Gaussian distribution over the user responses y with mean vector Mf and covariance matrix σ 2 I, wherein I is an m x m identity matrix, and M is a m x n matrix having only zero elements except for the elements k,u k = 1 and k,v k = -1; - obtaining a prior distribution over the function values of the user's internal response function using a Gaussian process; - using Bayes rule to obtain an analytical posterior distribution over the function values of the user's internal response function; - deriving an analytical expression for the predictive distribution based on the analytical expression of the posterior distribution over the function values of the user's internal response function; and - using the analytical expression for the predictive distribution to find the parameter value settings that the user prefers. - providing a set of parameters x 1, x 2,..., to be optimized; - providing a first sound based on a first parameter value setting x 1 = [x 11, x 21,.., x d1] and providing a second sound based on a second parameter value setting x 2 = [x 12, x 22,..., xd 2]; - prompting a user to rate said first and second sounds relative to each other; - providing a user response y 1 representing the user's rating of the two sounds relative to each other; - providing m user responses y = [y 1, y 2,..., y m] wherein each of the user responses represent the user's rating of two sounds relative to each other and wherein the sounds are derived from a multitude of n parameter value settings x 1, x 2,..., x n; - defining a Gaussian likelihood function as: p y k | f x u, f x v, σ 2 = N z k | f x u − f x v, σ 2 wherein f ( x u) and f ( x v) represent specific function values of the user's internal response function f, wherein y k represents a specific user response, wherein σ 2 represents the variance of the user response and wherein N y k | f x u − f x v, σ 2 is the single variate Gaussian distribution over the variable y k with mean value f ( x u) - f ( x v) and variance σ 2; - defining the likelihood as: p y | f = ∏ k = 1 m N y k | f x u − f x v, σ 2 = N y | Mf, σ 2 I wherein N y | Mf, σ 2 I m × m represents a multivariate Gaussian distribution over the user responses y with mean vector Mf and covariance matrix σ 2 I, wherein I is an m x m identity matrix, and M is a m x n matrix having only zero elements except for the elements k,u k = 1 and k,v k = -1; - obtaining a prior distribution over the function values of the user's internal response function using a Gaussian process; - using Bayes rule to obtain an analytical posterior distribution over the function values of the user's internal response function; - deriving an analytical expression for the predictive distribution based on the analytical expression of the posterior distribution over the function values of the user's internal response function; and - using the analytical expression for the predictive distribution to find the parameter value settings that the user prefers.