System for controlling a technical system based on an output which is obtained by applying a probabilistic model of the technical system to an input, wherein the technical system is a robot, a vehicle, a domestic appliance, a power tool, a manufacturing machine, a personal assistant, an access control system or a car engine, the probabilistic model comprising one or more layers, a layer of the one or more layers comprising multiple Gaussian processes, the system comprising: - a data interface for accessing a set of parameters of the probabilistic model, wherein the probabilistic model has been learned to predict one or more physical output quantities of the technical system based on at least one or more physical input quantities obtained from one or more sensors, wherein the set of parameters comprises at least: - a set of multiple inducing locations for the multiple Gaussian processes; - parameters of a probability distribution of a variational inference approximation of outputs of the multiple Gaussian processes at the multiple inducing locations, wherein the probability distribution comprises a multivariate normal probability distribution, a covariance matrix of the multivariate normal distribution being defined by a Kronecker product of a first matrix indicating a similarity between the multiple Gaussian processes and a second matrix indicating a similarity between the inducing locations, the first matrix and the second matrix being defined by variational parameters of the variational inference approximation; - one or more interfaces for accessing the input, wherein the input is obtained from one or more sensors; - a processor subsystem arranged to determine a sample of an output of the probabilistic model on the input based on the set of parameters of the probabilistic model; and - a control interface configured for controlling the technical system based on the output of the probabilistic model. - a data interface for accessing a set of parameters of the probabilistic model, wherein the probabilistic model has been learned to predict one or more physical output quantities of the technical system based on at least one or more physical input quantities obtained from one or more sensors, wherein the set of parameters comprises at least: - a set of multiple inducing locations for the multiple Gaussian processes; - parameters of a probability distribution of a variational inference approximation of outputs of the multiple Gaussian processes at the multiple inducing locations, wherein the probability distribution comprises a multivariate normal probability distribution, a covariance matrix of the multivariate normal distribution being defined by a Kronecker product of a first matrix indicating a similarity between the multiple Gaussian processes and a second matrix indicating a similarity between the inducing locations, the first matrix and the second matrix being defined by variational parameters of the variational inference approximation; - one or more interfaces for accessing the input, wherein the input is obtained from one or more sensors; - a processor subsystem arranged to determine a sample of an output of the probabilistic model on the input based on the set of parameters of the probabilistic model; and - a control interface configured for controlling the technical system based on the output of the probabilistic model. - a set of multiple inducing locations for the multiple Gaussian processes; - parameters of a probability distribution of a variational inference approximation of outputs of the multiple Gaussian processes at the multiple inducing locations, wherein the probability distribution comprises a multivariate normal probability distribution, a covariance matrix of the multivariate normal distribution being defined by a Kronecker product of a first matrix indicating a similarity between the multiple Gaussian processes and a second matrix indicating a similarity between the inducing locations, the first matrix and the second matrix being defined by variational parameters of the variational inference approximation; - one or more interfaces for accessing the input, wherein the input is obtained from one or more sensors; - a processor subsystem arranged to determine a sample of an output of the probabilistic model on the input based on the set of parameters of the probabilistic model; and - a control interface configured for controlling the technical system based on the output of the probabilistic model.