PROBABILITY CONTEXTUALIZATION
    6.
    发明申请

    公开(公告)号:US20220261671A1

    公开(公告)日:2022-08-18

    申请号:US17176209

    申请日:2021-02-16

    IPC分类号: G06N7/00 G06F16/242

    摘要: In an approach to explaining probabilistic answers through contextualization, one or more computer processors receive a query associated with a probability value of a first event from a user. One or more computer processors parse the query into one or more constituent parts. Based on the one or more constituent parts, one or more computer processors determine the first event. One or more computer processors query a probability value of the first event, where the second event is similar to the first event. One or more computer processors determine the probability value of the first event and the probability value of the second event are known. One or more computer processors fetch the probability value of the first event and the probability value of the second event. One or more computer processors display the probability value of the first event and the probability value of the second event.

    SELF-IMPROVING BAYESIAN NETWORK LEARNING

    公开(公告)号:US20220188693A1

    公开(公告)日:2022-06-16

    申请号:US17122038

    申请日:2020-12-15

    IPC分类号: G06N20/00 G06N7/00

    摘要: A method, a computer system, and a computer program product for creating multiple models asynchronously is provided. Embodiments of the present invention may include receiving input data, wherein input data includes a full training dataset. Embodiments of the present invention may include building, asynchronously, one or more Bayesian network models using one or more portions of the input data on a first pipeline and building a free learning model using the full training dataset on a second pipeline. Embodiments of the present invention may include retrieving the one or more Bayesian network models from the first pipeline. Embodiments of the present invention may include retrieving the free learning model from the second pipeline.