Abstract: An installed product includes mechanical components and associated sensors. A computer is programmed with an operational optimization model for the installed product, including modeling of the mechanical components and sensors. The computer is also programmed with a probability estimation model that provides an estimate of a difference measure related to input and output of the installed product. Sensor data is received, which had been generated during operation of the installed product. Output is generated from the operational optimization model based on the received sensor data. Output is generated from the probability estimation model based at least in part on the output from the operational optimization model. At least one installed product component is modified based at least in part on the output from the probability estimation model.

Abstract: According to some embodiments, a system includes a communication device operative to communicate with a user to receive a data set including a plurality of samples at a clustering module; a clustering module to receive the data set, store the data set, and calculate one or more clusters of samples using a clustering strategy; an optimization module to receive and store the one or more clusters of samples from the clustering module and generate one or more samples from the one or more clusters of samples using an optimization strategy; a memory for storing program instructions; at least one sample selection platform processor, coupled to the memory, and in communication with the clustering module and the optimization module and operative to execute program instructions to: calculate one or more clusters of samples based on the clustering strategy by executing the clustering module; analyze the data associated with the one or more clusters received from the clustering module using the optimization strategy asso

Abstract: A method and system for an agent-based evidential reasoning decision computer system for determining an adoption rate of a trend is provided. The system includes a plurality of nodes arranged in a tree structure. The plurality of nodes define an evidential reasoning algorithm where lower level nodes receive factors to be considered in the decision and each node assigns a likelihood of an outcome of the received factors, and generates an output to a subsequent higher level node or root of the tree structure. The system also includes a plurality of agent models organized in a hierarchical structure, each agent model comprising a respective set of the plurality of nodes and an output of the agent model, each agent model representing a member of a population, and an aggregator algorithm configured to combine the outputs of the plurality of agent models to generate an output representing an adoption rate.