Abstract: Methods and apparatus to determine probabilistic media viewing metrics are disclosed herein. An example apparatus includes memory including machine reachable instructions; and processor circuitry to execute the instructions to calculate a first probability for respective ones of a plurality of panelists as having viewed media based on viewing data, the viewing data including incomplete viewing data for one or more of the panelists relative to the media; identify respective ones of a plurality of panelists as included in a demographic subgroup based on demographic data for the panelists; assign a sampling weight to the respective ones of the plurality of panelists based on the demographic data; and calculate a second probability of the demographic subgroup having viewed the media based on the first probabilities and the sampling weights for the respective ones of the plurality of panelists in the demographic subgroup.

Abstract: Methods and apparatus to determine probabilistic media viewing metrics are disclosed herein. An example apparatus for determining a viewing metric for media to be viewed by a plurality of panelists includes a probability identifier to identify a probability for respective ones of the panelists with respect to the panelists viewing the media. The probability identifier is to identify the probability based on viewing data for the respective ones of the panelists. The example apparatus includes a calculator to calculate the viewing metric for the media based on the probabilities for the respective ones of the panelists and a sampling weight assigned to the respective ones of the panelists.

Abstract: Methods and apparatus to monitor media content at a content display site are described. An example method includes assigning a first set of weights to each of a plurality of panelists based on a set of collected characteristics for each person of a subset of unmeasured persons at unmeasured locations, the subset of unmeasured persons being the unmeasured person for which the collected characteristics are know selecting a subset of the panelists based on the first set of weights, each panelist of the subset of panelists selected to be representative of an unmeasured person of the unmeasured persons, re-weighting the subset of the panelists based on estimated characteristics of the unmeasured persons at the unmeasured locations to generate a second set of weights, generating a virtual panel.

Abstract: Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. Example instructions cause a machine to at least, based on impression requests from a plurality of client devices via a network, log a plurality of impressions corresponding to media accessed at the client devices; obtain a count of demographic impressions logged by a database proprietor; obtain a count of registered users of the database proprietor exposed to the media; and execute a process to determine a unique audience size by multiplying a count of the plurality of impressions by a square of the count of the registered users to generate a product; dividing the product by the count of the demographic impressions to generate a quotient; and determining the unique audience size based on a square root of the quotient.

Abstract: Methods and apparatus to monitor media content at a content display site are described.

Abstract: Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. Example instructions cause a machine to at least, based on impression requests from a plurality of client devices via a network, log a plurality of impressions corresponding to media accessed at the client devices; obtain a count of demographic impressions logged by a database proprietor; obtain a count of registered users of the database proprietor exposed to the media; and execute a process to determine a unique audience size by multiplying a count of the plurality of impressions by a square of the count of the registered users to generate a product; dividing the product by the count of the demographic impressions to generate a quotient; and determining the unique audience size based on a square root of the quotient.

Abstract: Methods, apparatus, systems to determine a conditional probability based on audience member probability distributions for media audience measurement are disclosed. Disclosed example methods for media audience measurement include determining a first audience probability distribution for a first member of a household and determining a second audience probability distribution for a second member of the household. Disclosed example methods also include calculating probabilities for audience combinations of the first member and the second member of the household based on the first audience probability distribution and the second audience probability distribution. Disclosed example methods further include determining a household audience characteristic probability based on the calculated probabilities of the audience combinations of the household. The household audience characteristic indicates likelihoods of different possible audience compositions of the household for a media event.

Abstract: Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. Example instructions cause a machine to at least, based on impression requests from a plurality of client devices via a network, log a plurality of impressions corresponding to media accessed at the client devices; obtain a count of demographic impressions logged by a database proprietor; obtain a count of registered users of the database proprietor exposed to the media; and execute a process to determine a unique audience size by multiplying a count of the plurality of impressions by a square of the count of the registered users to generate a product; dividing the product by the count of the demographic impressions to generate a quotient; and determining the unique audience size based on a square root of the quotient.

Abstract: Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. An example method includes receiving impression requests at a server from a plurality of client devices via a network; based on the impression requests, logging, with the server, a plurality of impressions corresponding to the media accessed at the client devices; obtaining a number of demographic impressions logged by a database proprietor; obtaining a number of registered users of the database proprietor exposed to the media; multiplying, by executing an instruction with a processor, a number of the plurality of impressions by a square of the number of the registered users to generate a product; dividing, by executing an instruction with the processor, the product by a number of the demographic impressions to generate a quotient; and determining, by executing an instruction with the processor, the unique audience based on a square root of the quotient.

Abstract: Methods, apparatus, systems to determine a conditional probability based on audience member probability distributions for media audience measurement are disclosed. Disclosed example methods for media audience measurement include determining a first audience probability distribution for a first member of a household and determining a second audience probability distribution for a second member of the household. Disclosed example methods also include calculating probabilities for audience combinations of the first member and the second member of the household based on the first audience probability distribution and the second audience probability distribution. Disclosed example methods further include determining a household audience characteristic probability based on the calculated probabilities of the audience combinations of the household. The household audience characteristic indicates likelihoods of different possible audience compositions of the household for a media event.

Abstract: Methods and apparatus to determine probabilistic media viewing metrics are disclosed herein. An example apparatus for determining a viewing metric for media to be viewed by a plurality of panelists includes a probability identifier to identify a probability for respective ones of the panelists with respect to the panelists viewing the media. The probability identifier is to identify the probability based on viewing data for the respective ones of the panelists. The example apparatus includes a calculator to calculate the viewing metric for the media based on the probabilities for the respective ones of the panelists and a sampling weight assigned to the respective ones of the panelists.

Abstract: Example methods disclosed herein include accessing a query requesting a metric associated with a number of unique members of an audience of media over an aggregate monitoring interval corresponding to a plurality of component monitoring intervals. Disclosed example methods also include determining respective aggregate interval probability distributions modeling likelihoods of respective monitored individuals being exposed to the media during the aggregate monitoring interval, a first one of the aggregate interval probability distributions for a first one of the monitored individuals being determined by combining parameters of respective component interval probability distributions modeling likelihoods of the first one of the monitored individuals being exposed to the media during respective ones of the component monitoring intervals.

Abstract: Methods, apparatus, systems to determine a conditional probability based on audience member probability distributions for media audience measurement are disclosed. Disclosed example methods for media audience measurement include determining a first audience probability distribution for a first member of a household and determining a second audience probability distribution for a second member of the household. Disclosed example methods also include calculating probabilities for audience combinations of the first member and the second member of the household based on the first audience probability distribution and the second audience probability distribution. Disclosed example methods further include determining a household audience characteristic probability based on the calculated probabilities of the audience combinations of the household. The household audience characteristic indicates likelihoods of different possible audience compositions of the household for a media event.

Abstract: Methods, apparatus, systems and articles of manufacture to de-duplicate partially-tagged entities are disclosed. An example method includes identifying a tagged audience for a first sub-entity, identifying a panel audience for the second sub-entity, determining a panel duplication between the first sub-entity and a second sub-entity, determining a duplicated audience based on the tagged audience, the panel audience, and the panel duplication, and determining a de-duplicated audience for the partially-tagged entity based on the duplicated audience and a total audience, the total audience including the tagged audience for the first sub-entity and the panel audience for the second sub-entity.

Abstract: Methods and apparatus to monitor media content at a content display site are described.

Abstract: Methods and apparatus to monitor media content at a content display site are described. An example method includes obtaining panelist data corresponding to a plurality of panelists accessing web pages at measured locations, classifying the panelist data according to demographic information of the panelists, generating a virtual panel based on an estimate of web page access at unmeasured locations, and classifying page view data associated with the unmeasured locations based on data corresponding to the virtual panel.

Abstract: Methods and apparatus to monitor media content at a content display site are described. An example method includes obtaining panelist data corresponding to a plurality of panelists accessing web pages at measured locations, classifying the panelist data according to demographic information of the panelists, generating a virtual panel based on an estimate of web page access at unmeasured locations, and classifying page view data associated with the unmeasured locations based on data corresponding to the virtual panel.