1. A computer-implemented method for event predicting via machine learning based on multi-source heterogeneous data, comprising: acquiring at least two types of historical data associated with an event result for a first event of a predetermined type; establishing a joint likelihood model of attribute data of the first event and the at least two types of historical data; determining an optimal estimation of the attribute data based on the joint likelihood model according to a maximum posterior principle; and determining a parameter in a probability distribution as a prediction result of a second event based on the probability distribution associated with the attribute data in the joint likelihood model, wherein the joint likelihood model includes one or more adjustment parameters for correcting the joint likelihood model, the adjustment parameters being determined iteratively based on an accuracy of the prediction result.