8. A system for modeling event data using a pre-existing taxonomy of events, the event data representing a plurality of sequences of events, each sequence comprising an order of events initiated by a corresponding user, each event mapping to a leaf node of the taxonomy, the system comprising one or more computing devices configured to: identify a plurality of candidate Markov models representing the probabilities of a user transitioning from any first node in the model to any second node in the model according to the sequences of events, each Markov model formed from a subset of nodes in the taxonomy by merging selected nodes of the taxonomy into corresponding ancestor nodes of the taxonomy, wherein each event is represented by a node in each Markov model, further wherein no Markov model contains both a particular node and an ancestor of that particular node; measure the fitness of the candidate Markov models with a fitness policy; select at least some of the plurality of candidate Markov models with reference to the fitness measure and one or more resource constraints; and choose a preferred Markov model from the selected candidate Markov models with reference to an objective function.
8. A system for modeling event data using a pre-existing taxonomy of events, the event data representing a plurality of sequences of events, each sequence comprising an order of events initiated by a corresponding user, each event mapping to a leaf node of the taxonomy, the system comprising one or more computing devices configured to: identify a plurality of candidate Markov models representing the probabilities of a user transitioning from any first node in the model to any second node in the model according to the sequences of events, each Markov model formed from a subset of nodes in the taxonomy by merging selected nodes of the taxonomy into corresponding ancestor nodes of the taxonomy, wherein each event is represented by a node in each Markov model, further wherein no Markov model contains both a particular node and an ancestor of that particular node; measure the fitness of the candidate Markov models with a fitness policy; select at least some of the plurality of candidate Markov models with reference to the fitness measure and one or more resource constraints; and choose a preferred Markov model from the selected candidate Markov models with reference to an objective function. 13. The system of claim 8 , wherein the one or more computing devices are further configured to display advertisements for a user based on a probability represented by the preferred Markov model.