10. The method according to claim 1 , wherein the crack growth model is given by: 
 da/dN=C (Δ K ) m where a is a crack size, N is the number of load cycles, C and m are model parameters identified from fatigue testing data and ΔK is a stress intensity factor range for one load cycle.
1. A method for probabilistic fatigue life prediction of a turbine part, comprising: in a fatigue life prediction processor, receiving a probability of detection model; in the fatigue life prediction processor, receiving an initial crack size probability density function (PDF) for the turbine part based on the received probability of detection model; in the fatigue life prediction processor, receiving probabilistic identification of model parameters; in the fatigue life prediction processor, receiving a model parameter PDF based on the received probabilistic identification of model parameters according to: 
 ƒ(ln C,m )˜MVN(μ (ln C,m) ,Σ (ln C,m) ), where MVN is a multivariate normal distribution, C and m are model parameters from fatigue testing data, μ (ln C,m) is a mean vector and Σ (ln C,m) is a covariance matrix; receiving a crack growth model based on the received initial crack size PDF, the model parameter PDF and material/load factors; determining uncertainty propagation using an analytical or simulation-based method based on the crack growth model based on uncertainty of at least one of material properties, geometries, sensitivity of instruments, and loading of the turbine part; in the fatigue life prediction processor, computing a fatigue life prediction for the turbine part based on the uncertainty propagation and crack growth model; and implementing a maintenance plan for the turbine part based on the fatigue life prediction.