A computer-implemented method for generating a prediction model for predicting rotor blade damages of a wind turbine, wherein the method processes previously acquired data (DA), said data (DA) comprising data sets (DS) for a plurality of wind turbines, where each data set (DS) refers to a specific wind turbine (WT) and comprises respective values of variables, the variables including one or more turbine variables (T1, T2, ..., T10) defining characteristics of the specific wind turbine (WT), one or more weather variables (W1, W2, ..., W4) defining weather conditions averaged over the operation time of the specific wind turbine (WT) and one or more damage variables (D1, D2, ..., D19) defining damages having occurred on at least one rotor blade of the specific wind turbine (WT) during the operation time of the specific wind turbine (WT), wherein the method comprises the following steps: a) discretizing the values of those variables which are numerical variables, resulting in modified data sets (DS'); b) structure learning of a plurality of Bayesian networks (BN1, BN2 ..., BNN) based on the modified data sets (DS'), where each Bayesian network (BN1, BN2 ..., BNN) is a probabilistic directed acyclic graph comprising the variables as nodes and defining the probabilistic dependencies between nodes by directed edges (E) and where each Bayesian network (BN1, BN2 ..., BNN) is learned by another learning method; c) determining an optimum Bayesian network (OBN) out of the plurality of Bayesian networks (BN1, BN2 ..., BNN) based on a performance measure (PM) reflecting the prediction quality of a respective Bayesian network (BN1, BN2 ..., BNN), where the optimum Bayesian network (OBN) has the best performance measure (PM); d) parameter learning of the optimum Bayesian network (OBN) based on the modified data sets (DS'), resulting in conditional probabilities (CP) between variables representing nodes linked by respective directed edges (E) in the optimum Bayesian network (OBN), where the optimum Bayesian network (OBN) in combination with the conditional probabilities (CP) is the prediction model. a) discretizing the values of those variables which are numerical variables, resulting in modified data sets (DS'); b) structure learning of a plurality of Bayesian networks (BN1, BN2 ..., BNN) based on the modified data sets (DS'), where each Bayesian network (BN1, BN2 ..., BNN) is a probabilistic directed acyclic graph comprising the variables as nodes and defining the probabilistic dependencies between nodes by directed edges (E) and where each Bayesian network (BN1, BN2 ..., BNN) is learned by another learning method; c) determining an optimum Bayesian network (OBN) out of the plurality of Bayesian networks (BN1, BN2 ..., BNN) based on a performance measure (PM) reflecting the prediction quality of a respective Bayesian network (BN1, BN2 ..., BNN), where the optimum Bayesian network (OBN) has the best performance measure (PM); d) parameter learning of the optimum Bayesian network (OBN) based on the modified data sets (DS'), resulting in conditional probabilities (CP) between variables representing nodes linked by respective directed edges (E) in the optimum Bayesian network (OBN), where the optimum Bayesian network (OBN) in combination with the conditional probabilities (CP) is the prediction model. <EOS>