Techniques are disclosed herein for making predictions with respect to how content consumers will interact with a digital asset. For example, in the context of website visitors browsing digital assets provided via a website, web traffic data can be collected and modeled using a belief network. The belief network may represent a probability distribution for a set of variables that define the web traffic data. Examples of such variables include browser type, browsing session duration, geographic location, visitor demographic characteristics, and a browsing outcome. Certain of the embodiments disclosed herein can be used to extract knowledge from the belief network, thereby allowing statistical inferences to be drawn with respect to how certain classes of website visitors will interact with the website. The influence of one or more first variables (for example, geographic location) can be quantified with respect to one or more second variables (for example, the successful result indicator).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for deriving information from a belief network that is used to model web traffic data, the method comprising: providing a user interface configured to receive a selection of a given variable that is observed to take one or more given values in collected web traffic data, wherein the web traffic data is modeled using a belief network; extracting a plurality of variable-value pairs from the belief network, each extracted variable-value pair comprising an influential variable and a corresponding influential value, wherein each extracted variable-value pair has a statistical influence on a probability distribution of values taken by the given variable; and displaying a ranked list of the plurality of extracted variable-value pairs, the ranked list being ordered based on an influence parameter that quantifies the statistical influence a particular extracted variable-value pair has on the probability distribution of values taken by the given variable. 2. The method of claim 1, wherein the influence parameter corresponds to a normalized difference between (a) a conditional distribution of the given variable given the influential variable and the corresponding influential value and (b) a marginal distribution of the given variable. 3. The method of claim 1, wherein the influence parameter is determined before the selection of the given variable is received. 4. The method of claim 1, wherein the given variable corresponds to a success indicator based on a web browsing outcome and wherein the given value is a true/false flag. 5. The method of claim 1, further comprising extracting a plurality of secondary variable-value pairs from the belief network, each extracted secondary variable-value pair comprising a secondary influential variable and a corresponding secondary influential value, wherein a combination of a selected extracted variable-value pair and the extracted secondary variable-value pair has a combined statistical influence on the probability distribution that is greater than the statistical influence caused by the selected extracted variable-value pair. 6. The method of claim 1, wherein the influence parameter corresponds to a normalized difference between (a) a conditional distribution of the given variable given a plurality of influential variables and a corresponding plurality of influential values and (b) a marginal distribution of the given variable. 7. The method of claim 1, wherein the influential value comprises a range of values that, when taken by the influential variable, renders the statistical influence on the probability distribution of values taken by the given variable. 8. The method of claim 1, wherein the user interface is further configured to display a graphical representation of the belief network, the graphical representation including a plurality of nodes representing observed variables and a plurality of edges representing a correlation between connected nodes. 9. A system for deriving information from a belief network that is used to model web traffic data, the system comprising: a visualization module configured to provide a graphical representation of a belief network that is used to model web traffic data, the graphical representation including a plurality of nodes representing observed variables and a plurality of edges representing a correlation between connected nodes; a query interface configured to receive a selection of a given variable represented by one of the plurality of nodes; and a knowledge discovery module configured to identify a plurality of variable-value pairs from the belief network, each identified variable-value pair (a) comprising an influential variable and a corresponding influential value, and (b) having a statistical influence on a probability distribution of values taken by the given variable; wherein the visualization module is further configured to display a ranked list of the plurality of identified variable-value pairs, the ranked list being ordered based on an influence parameter that quantifies the statistical influence a particular identified variable-value pair has on the probability distribution of values taken by the given variable. 10. The system of claim 9, further comprising a visitor analytics engine that is connected to a website server via a network, wherein: the visitor analytics engine includes the visualization module, the query interface and the knowledge discovery module; and the web traffic data is generated as a result of website visitor interaction with content served by the website server. 11. The system of claim 9, wherein the web traffic data modeled using the belief network comprises: a first subset of records having known values for a plurality of variables; and a second subset of records having known values for a first subset of the plurality of variables and unknown values for a second subset of the plurality of variables. 12. The system of claim 9, wherein the given variable corresponds to a success indicator based on a web browsing outcome and wherein the given variable takes a value corresponding to a true/false flag. 13. The system of claim 9, wherein a selected two of the plurality of variable-value pairs identified by the knowledge discovery module have a combined statistical influence that is greater than the respective statistical influences of the selected two variable-value pairs taken individually. 14. The system of claim 9, wherein: a selected two of the plurality of variable-value pairs identified by the knowledge discovery module have a combined statistical influence that is greater that the respective statistical influences of the selected two variable-value pairs taken individually; and the ranked list is ordered based on a combined influence parameter that quantifies the combined statistical influence of the selected two of the plurality of variable-value pairs. 15. The system of claim 9, wherein the query interface works in conjunction with the visualization module, such that the selection of the given variable can be made by selecting one of the plurality of nodes provided in the graphical representation of the belief network. 16. A non-transient computer readable medium having instructions encoded thereon that, when executed by one or more processors, causes an information derivation process for a belief network to be carried out, the process comprising: providing a user interface configured to receive a selection of a given variable and a given value corresponding to the given variable, wherein the given variable is observed in web traffic data modeled using a belief network; determining a primary profile for a website visitor based on the given variable and the given value, wherein the primary profile comprises a plurality of variable-value pairs that are extracted from the belief network based on a correlation to the given variable and the given value; and displaying a list of the plurality of variable-value pairs. 17. The non-transient computer readable medium of claim 16, wherein the user interface is configured to receive a selection of a plurality of given varies and a corresponding plurality of given values. 18. The non-transient computer readable medium of claim 16, wherein the variable-value pairs comprise a variable extracted from the belief network and a range of values corresponding to the extracted variable. 19. The non-transient computer readable medium of claim 16, wherein the given variable corresponds to a success indicator based on a web browsing outcome and wherein the given value is a true/false flag. 20. The non-transient computer readable medium of claim 16, the process further comprising an alternative profile for the website visitor based on the given variable and the given value, wherein the alternative profile has a reduced probability as compared to the primary profile.