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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for media audience measurement, the apparatus comprising: a distribution determiner to: determine a first audience probability distribution for a first member of a household based on a quantity of household data stored in a memory; and determine a second audience probability distribution for a second member of the household based on the quantity of household data stored in the memory; a simulation averager to calculate a first probability for a first audience combination and a second probability for a second audience combination based on the first audience probability distribution and the second audience probability distribution; and an audience characteristic calculator to reduce the quantity of household data stored in the memory by determining a household audience characteristic probability based on the first audience combination and the second audience combination, the household audience characteristic indicating likelihoods of different possible audience compositions of the household for a media event. 2. The apparatus as defined in claim 1, wherein the distribution determiner is to: determine the first audience probability distribution based on a first count of a first audience parameter and a second count for a second audience parameter for the first member; and determine the second audience probability distribution based on a third count of the first audience parameter and a fourth count for the second audience parameter for the second member, the first audience parameter corresponding to exposure to the media event and the second audience parameter corresponding to non-exposure to the media event. 3. The apparatus as defined in claim 2, wherein the distribution determiner is to: determine a first beta distribution for the first member to determine the first audience probability distribution; and determine a second beta distribution for the second member to determine the second audience probability distribution. 4. The apparatus as defined in claim 1, wherein the distribution determiner is to: determine a first Dirichlet distribution for the first member to determine the first audience probability distribution, the first Dirichlet distribution being determined based on a first count of a first audience parameter, a second count for a second audience parameter, and a third count for a third audience parameter for the first member; and determine a second Dirichlet distribution for the second member determine the second audience probability distribution, the second Dirichlet distribution being determined based on a fourth count of the first audience parameter, a fifth count for the second audience parameter, and a sixth count for the third audience parameter for the second member, the first audience parameter corresponding to exposure to the media event via a first media presentation device of the household, the second audience parameter corresponding to exposure to the media event via a second media presentation device of the household, and the third audience parameter corresponds to non-exposure to the media event. 5. The apparatus as defined in claim 1, wherein, to determine the household audience characteristic probability, the audience characteristic calculator is to determine a conditional probability based on the first audience combination and the second audience combination, the conditional probability indicative of a probability of a second event given a first event has occurred. 6. The apparatus as defined in claim 1, further including a probability simulator to: perform a first set of simulations that includes a first simulation for the first member and a second simulation for the second member, the first simulation to randomly select a first value from the first audience probability distribution, the second simulation to randomly select a second value from the second audience probability distribution; calculate the first probability of the first audience combination based on the randomly-selected first value and the randomly-selected second value of the first set of simulations; perform a second set of simulations that includes a third simulation for the first member and a fourth simulation for the second member, the third simulation to randomly select a third value from the first audience probability distribution, the fourth simulation to randomly select a fourth value from the second audience probability distribution of the second set of simulations; and calculate a second probability of the first audience combination based on the randomly-selected third value and the randomly-selected fourth value, the simulation averager is to determine an average first audience combination probability based on the first probability and the second probability calculated by the probability simulator. 7. A method for media audience measurement, the method comprising: determining, by executing an instruction with one or more processors, a first audience probability distribution for a first member of a household based on a quantity of household data stored in memory; determining, by executing an instruction with the one or more processors, a second audience probability distribution for a second member of the household based on the quantity of household data stored in the memory; calculating, by executing an instruction with the one or more processors, a first probability for a first audience combination and a second probability for a second audience combination based on the first audience probability distribution and the second audience probability distribution; and reducing, by executing an instruction with the one or more processors, the quantity of household data stored in the memory by determining a household audience characteristic probability based on the first audience combination and the second audience combination the household audience characteristic indicating likelihoods of different possible audience compositions of the household for a media event. 8. The method as defined in claim 7, wherein: the determining of the first audience probability distribution is based on a first count of a first audience parameter and a second count for a second audience parameter for the first member; the determining of the second audience probability distribution is based on a third count of the first audience parameter and a fourth count for the second audience parameter for the second member; and the first audience parameter corresponds to exposure to the media event and the second audience parameter corresponds to non-exposure to the media event. 9. The method as defined in claim 8, wherein the determining of the first audience probability distribution includes determining a first beta distribution for the first member and the determining of the second audience probability distribution includes determining a second beta distribution for the second member. 10. The method as defined in claim 7, wherein: the determining of the first audience probability distribution includes determining a first Dirichlet distribution for the first member based on a first count of a first audience parameter, a second count for a second audience parameter, and a third count for a third audience parameter for the first member; the determining of the second audience probability distribution includes determining a second Dirichlet distribution for the second member based on a fourth count of the first audience parameter, a fifth count for the second audience parameter, and a sixth count for the third audience parameter for the second member; and the first audience parameter corresponds to exposure to the media event via a first media presentation device of the household, the second audience parameter corresponds to exposure to the media event via a second media presentation device of the household, and the third audience parameter corresponds to non-exposure to the media event. 11. The method as defined in claim 7, wherein the determining of the household audience characteristic probability includes determining a conditional probability based on the first audience combination and the second audience combination, the conditional probability indicative of a probability of a second event given a first event has occurred. 12. The method as defined in claim 7, wherein the calculating of the first probability for the first audience combination and the second probability for the second audience combination includes: performing a first set of simulations that includes a first simulation for the first member and a second simulation for the second member, the first simulation randomly selects a first value from the first audience probability distribution, the second simulation randomly selects a second value from the second audience probability distribution; calculating the first probability of the first audience combination based on the randomly-selected first value and the randomly-selected second value of the first set of simulations; performing a second set of simulations that includes a third simulation for the first member and a fourth simulation for the second member, the third simulation randomly selects a third value from the first audience probability distribution, the fourth simulation randomly selects a fourth value from the second audience probability distribution of the second set of simulations; calculating a second probability of the first audience combination based on the randomly-selected third value and the randomly-selected fourth value; and determining an average first audience combination probability based on the first probability and the second probability. 13. The method as defined in claim 7, wherein the determining of the household audience characteristic probability based on the first audience probability distribution and the second audience probability distribution reduces an amount of data collected by computer networked data collection systems for the first member and the second member of the household. 14. The method as defined in claim 7, wherein the one or more processors include at least a first processor of a first hardware computer system and a second processor of a second hardware computer system. 15. A tangible computer readable storage medium comprising instructions which, when executed, cause a machine to at least: determine a first audience probability distribution for a first member of a household based on a quantity of household data stored in a memory; determine a second audience probability distribution for a second member of the household based on the quantity of household data stored in the memory; calculate a first probability for a first audience combination and a second probability for a second audience combination based on the first audience probability distribution and the second audience probability distribution; and reduce the quantity of household data stored in the memory by determining a household audience characteristic probability based on the first audience combination and the second audience combination, the household audience characteristic indicating likelihoods of different possible audience compositions of the household for a media event. 16. The tangible computer readable storage medium as defined in claim 15, the instructions further cause the machine to: determine the first audience probability distribution based on a first count of a first audience parameter and a second count for a second audience parameter for the first member; and determine the second audience probability distribution based on a third count of the first audience parameter and a fourth count for the second audience parameter for the second member, the first audience parameter corresponding to exposure to the media event and the second audience parameter corresponding to non-exposure to the media event. 17. The tangible computer readable storage medium as defined in claim 16, wherein the instructions further cause the machine to: determine a first beta distribution for the first member to determine the first audience probability distribution; and determine a second beta distribution for the second member to determine the second audience probability distribution. 18. The tangible computer readable storage medium as defined in claim 15, wherein the instructions further cause the machine to: determine a first Dirichlet distribution for the first member to determine the first audience probability distribution, the first Dirichlet distribution based being determined based on a first count of a first audience parameter, a second count for a second audience parameter, and a third count for a third audience parameter for the first member; and determine a second Dirichlet distribution for the second member to determine the second audience probability distribution, the second Dirichlet distribution being based on a fourth count of the first audience parameter, a fifth count for the second audience parameter, and a sixth count for the third audience parameter for the second member, the first audience parameter corresponding to exposure to the media event via a first media presentation device of the household, the second audience parameter corresponding to exposure to the media event via a second media presentation device of the household, and the third audience parameter corresponds to non-exposure to the media event. 19. The tangible computer readable storage medium as defined in claim 15, wherein, to determine the household audience characteristic probability, the instructions further cause the machine to determine a conditional probability based on the first audience combination and the second audience combination, the conditional probability indicative of a probability of a second event given a first event has occurred. 20. The tangible computer readable storage medium as defined in claim 15, wherein, to calculate the first probability for the first audience combination and the second probability for the second audience combination, the instructions further cause the machine to: perform a first set of simulations that includes a first simulation for the first member and a second simulation for the second member, the first simulation to randomly select a first value from the first audience probability distribution, the second simulation to randomly select a second value from the second audience probability distribution; calculate the first probability of the first audience combination based on the randomly-selected first value and the randomly-selected second value of the first set of simulations; perform a second set of simulations that includes a third simulation for the first member and a fourth simulation for the second member, the third simulation to randomly select a third value from the first audience probability distribution, the fourth simulation to randomly select a fourth value from the second audience probability distribution of the second set of simulations; calculate a second probability of the first audience combination based on the randomly-selected third value and the randomly-selected fourth value; and determine an average first audience combination probability based on the first probability and the second probability.