Abstract: An example computer system for implementing controls for a cloud computing environment can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create: a repository engine programmed to consolidate the controls for the cloud computing environment; a workload engine programmed to determine an applicability of the controls to data stored in the cloud computing environment; an adherence validation engine programmed to validate compliance of the controls of the cloud computing environment with the control requirements; a security risk engine programmed to assess security risks associated with the cloud computing environment; and an adherence monitoring engine programmed to continuously monitor compliance of the cloud computing environment with the control requirements and measure changes to the security risks.

Abstract: Methods and apparatus are disclosed to model consumer choices.

Abstract: Methods and apparatus are disclosed to model consumer choices.

Abstract: Methods, systems and apparatus are disclosed to model consumer choices. An example apparatus includes a multinomial logit (MNL) engine to add a set of products having respondent choice data to a base MNL model, an aggregate building engine to improve a computational efficiency of model generation by generating a number of copies of the base MNL model, each one of the number of copies of the base MNL model exhibiting an effect of an independence or irrelevant alternatives (HA) property, a sourcing modifier to proportionally affect interrelationships between dissimilar ones of the number of products in the set by inserting sourcing effect values in the aggregate model, an estimator to estimate the item utility parameters of the aggregate model based on the number of copies of the base MNL model and the respondent choice data, and a simulation engine to calculate the choice probability.

Abstract: Methods, systems and apparatus are disclosed to model consumer choices. An example apparatus includes a multinomial logit (MNL) engine to add a set of products having respondent choice data to a base MNL model, an aggregate building engine to improve a computational efficiency of model generation by generating a number of copies of the base MNL model, each one of the number of copies of the base MNL model exhibiting an effect of an independence or irrelevant alternatives (HA) property, a sourcing modifier to proportionally affect interrelationships between dissimilar ones of the number of products in the set by inserting sourcing effect values in the aggregate model, an estimator to estimate the item utility parameters of the aggregate model based on the number of copies of the base MNL model and the respondent choice data, and a simulation engine to calculate the choice probability.

Abstract: Methods, systems and apparatus are disclosed to model consumer choices.

Abstract: Methods, systems and apparatus are disclosed to model consumer choices.

Abstract: Methods and apparatus are disclosed to model consumer choices.

Abstract: Methods and apparatus are disclosed to model consumer choices. An example method includes identifying a set of products, receiving respondent choice data associated with the set of products, and adding the set of products to a base multinomial logit (MNL) model. The example method also includes generating, with a programmed processor, a number of copies of the MNL model to form an aggregate model based on a number of products in the set, each copy including an item utility parameter for each product in the set of products, and creating a matrix structure based on the number of products in the set, the matrix structure to be subtracted from each item utility parameter in the aggregate model. Further, the example method includes estimating each item utility parameter of the aggregate model and the matrix structure based on the number of copies of the MNL model and the respondent choice data, and calculating a choice probability based on each of the estimated utility parameters.

Abstract: Methods and apparatus are disclosed to perform choice modeling with substitutability data. An example method includes receiving base choice probability values for a respondent, wherein the base choice probability value is associated with a product, receiving a respondent substitutability factor associated with the product, identifying, with a cluster analysis engine, a primary product and a secondary product and generating a subrespondent associated with the secondary product, and calculating, with a cross sourcing engine, a modified choice probability for the subrespondent for the secondary product based on the respondent substitutability factor and the base choice probability values associated with the secondary product.

Abstract: Example methods and apparatus to model with ghost respondents are disclosed. A disclosed example method includes estimating discrete choice utility values for a plurality of respondents based on a plurality of market-available products, and dividing the plurality of respondents into groups based on an ownership status of the plurality of market-available products. The example method also includes identifying a test starter product from the plurality of market-available products based on test criteria indicative of a degree of similarity with the new product, generating a ghost group associated with the new product, and assigning utility values of the test starter product to the new product in the ghost group. Additionally, the example method includes tailoring the utility values assigned to the new product with a ghost group utility adjustment rule, and generating a ghost group model to represent consumers of the new product.

Abstract: An archery bow lock mechanism allows a compound bow to be locked in the fully drawn position and released by the archer upon the application of a small amount of additional draw tension. The device utilizes a pair of pawl and ratchet members which approach each other and engage when the bow is drawn. Upon release of the drawstring the cam and ratchet members, which may be in the form of a ball and triangular ratchet, respectively, located on the bow cables, remain engaged, the ball being maintained in contact with a concave face of the ratchet by drawstring tension. Additional tension on the bow string disengages the pawl and ratchet, allowing the bow to be fired. Alternative positions for the ratchet and pawl members are available.