A computer-implemented method of predicting sensor output values of a sensor monitoring system, comprising the steps of: providing a set of one or more test input values to a system of sensors, and one or more known sensor output values from said sensor system, wherein other sensor output values are unknown; calculating, from a generative model, a predictive Gaussian distribution function for all of the unknown output sensor values P y U | x = ∏ m ∈ U P y m | x = 1 2 π d / 2 S y U 1 / 2 exp − 1 2 y U − μ y U T S y U − 1 y U − μ y U from the test input values, wherein vector x of dimensionality represents the test input values, vector y U of dimensionality M represents the set of unknown output sensor values, y m ∈ y U, μ y U is a vector of mean values of the set y U determined by a training phase, an S y U is a diagonal covariance matrix of the set y U determined by said training phase; and predicting each unknown output sensor value y m from P y m | x, y O = ∫ y U m ̅ P y m | x, y U m ̅, y O P y U m ̅ | x, y O d y U m ̅ by Bayesian integration, where vector y o represents the known sensor output values and vector y U m represents unknown output sensor values in y U except y m,; wherein P ( y m | x, y U m, y o) is modeled by a conditional model to yield a first Gaussian distribution N y m | μ y m, s y m = 1 2 π d / 2 s y m 1 / 2 exp − 1 2 y m − μ y m T s y m − 1 y m − μ y m, in which μ ym = ∑ i = 1 N m β i k z, z m, i | Λ z and s y m = σ y m 2 − ∑ i = 1 N m γ ij k z, z m, j | Λ z k z, z m, j | Λ z, where z is a combined vector of x T y O T y U m ̅ T T and k z, z m, n | Λ z = k x, x m, n | Λ x k y O, y O, n | Λ y O, n k y U m ̅, y U m ̅, n | Λ y U m ̅, and where z m,i are all known training data, and P ( y U m | x, y O) is modeled by the generative model to yield a second Gaussian distribution corresponding to the predictive Gaussian distribution for the set of unknown output sensor values except y m: P y U m ̅ | x, y O = N y U m ̅ | μ y U m ̅, S y U m ̅ ∝ k y U m ̅, μ y U m ̅ | S y U m ̅, where Sy U m denotes the diagonal covariance matrix of the set of unknown sensor values except y m. providing a set of one or more test input values to a system of sensors, and one or more known sensor output values from said sensor system, wherein other sensor output values are unknown; calculating, from a generative model, a predictive Gaussian distribution function for all of the unknown output sensor values P y U | x = ∏ m ∈ U P y m | x = 1 2 π d / 2 S y U 1 / 2 exp − 1 2 y U − μ y U T S y U − 1 y U − μ y U from the test input values, wherein vector x of dimensionality represents the test input values, vector y U of dimensionality M represents the set of unknown output sensor values, y m ∈ y U, μ y U is a vector of mean values of the set y U determined by a training phase, an S y U is a diagonal covariance matrix of the set y U determined by said training phase; and predicting each unknown output sensor value y m from P y m | x, y O = ∫ y U m ̅ P y m | x, y U m ̅, y O P y U m ̅ | x, y O d y U m ̅ by Bayesian integration, where vector y o represents the known sensor output values and vector y U m represents unknown output sensor values in y U except y m,;