Abstract:
A method, apparatus and computer program product for performing probabilistic inference and providing related solution methods is presented. At least one state space (SS) is obtained for variables of interest relating to a problem of interest. None or more densities (D) defining pure functions over locations in the at least one SS are also obtained as is none or more kernels (K) defining a stochastic walk through the at least one SS. A virtual machine executes a stochastic walk through the state space to produce a solution for a problem of interest.
Abstract:
Example methods and systems for performing fleet planning based on coarse estimates of regions is provided. A method may include receiving information indicative of a sequence of coverage requirements for a region over a period of time. For one or more time intervals of the period of time, the method may include dividing the region over which vehicles of the plurality of vehicles may traverse into a plurality of sub-regions such that for each subsequent time interval a size of a given sub-region increases. The method includes at each of the one or more time intervals of the period of time, determining vehicles of the plurality of vehicles that can reach a given landmark in a given sub-region by an end of the one or more time intervals, and based on the sequence of coverage requirements, generating a fleet plan for the time intervals based on the determined vehicles.
Abstract:
A method, apparatus and computer program product for performing probabilistic inference and providing related solution methods is presented. At least one state space (SS) is obtained for variables of interest relating to a problem of interest. None or more densities (D) defining pure functions over locations in the at least one SS are also obtained as is none or more kernels (K) defining a stochastic walk through the at least one SS. A virtual machine executes a stochastic walk through the state space to produce a solution for a problem of interest.