6 results about "Conditional independence" patented technology

The invention provides a data processing method and device based on clinical test data, in the application, a prior probability and a historical likelihood of a symptom corresponding to target clinical test data can be determined through a historical clinical database, the prior probability reflects a basic epidemic rate of an indication in a population, and the historical likelihood of the symptom corresponding to the target clinical test data can be determined through the historical clinical database. According to the historical likelihood, association rules between symptoms and indications are mined from historical cases, the association rules and the indications are dynamically updated through a Bayesian formula, an inference chain conforming to clinical logic is formed, then, a complex multi-feature joint probability estimation problem is converted into product calculation of single-feature statistics through conditional independent assumption, and a probability estimation result is obtained. The problem of calculation feasibility under high-dimensional data is solved, probabilistic output provides a quantitative basis for auxiliary determination of indications, a data-driven statistical rule is converted into a clinically understandable auxiliary support tool, and objectivity, consistency and scientificity of diagnosis decisions are effectively improved.

The application discloses a power grid fault position inference method and system, comprising factorization of conditional probability distribution functions of network topology Y of given multi-source feedback data to obtain conditionally independent fault factors; parameterization of the fault factors according to available historical statistical interruption information, establishment of a Bayesian network for each distribution feeder; and inference of fault positions by performing an inference task on the Bayesian network by using a sampling algorithm. The application adopts a probabilistic graphical modeling method for data fusion, and a high-dimensional joint probability distribution function of the system is decomposed into a group of more easily managed probability factors, which are obtained by conditional independence, so as to reduce the calculation complexity of the high-dimensional joint probability distribution function representing the system and improve the robustness and accuracy of a fault research and judgment framework.

This application discloses a reliable AI scheduling method, system, and computer equipment for microgrids. It includes: conditional independence testing of multi-source data; constructing a candidate causal graph among variables, constrained by prior knowledge in the power sector; calculating the differences in causal effects caused by fluctuations in the current scheduling strategy; performing counterfactual reasoning verification based on different differentiated scheduling strategies; and scoring the current scheduling strategy on causal interpretability based on causal effect differences, counterfactual verification results, and link integrity, followed by AI scheduling. This application reveals the causal interaction mechanism between variables, and the counterfactual reasoning that changes in variables lead to changes in scheduling results, improving the interpretability of the scheduling strategy. It can trace the causal driving factors of each scheduling instruction, significantly reducing the rate of human intervention. Prior knowledge in the power sector ensures safe and compliant scheduling, making the results more reliable. It solves the problem of dependence on human intervention in the application of AI scheduling in microgrids.

A method for urban multivariate spatiotemporal prediction based on a large model agent belongs to the field of smart city technology. In order to solve the problem of difficulty in predicting urban operating conditions under incomplete data, the present invention includes defining urban multivariate spatiotemporal prediction as the prediction of a discrete random process, setting the probability of the system state at each moment as the conditional independence hypothesis and the Markov hypothesis; performing state decoupling, decoupling the probability prediction of the system state into the prediction of each channel state given the previous system state; constructing a spatial region, which includes a domain agent layer, a spatiotemporal agent layer, and an individual agent layer; constructing an urban multivariate spatiotemporal prediction system based on a large model agent, including constructing an action space, a state space, a transfer function, a reward function, and a memory flow; and performing task prediction on the constructed urban multivariate spatiotemporal prediction system based on a large model agent. The present invention can improve the accuracy and efficiency of urban management.

The application provides a software defect probability determination method, device, equipment and storage medium. The method comprises the following steps: determining a target defect type to be predicted of a current version of target software, then inputting defect probabilities corresponding to a plurality of defect types of the previous N versions of the current version into a prediction model trained in advance to obtain a first posterior probability corresponding to the target defect type, and then determining a defect probability corresponding to the target defect type of the current version according to the first posterior probability. The prediction model can be a multiple neural network, and the input and output of the multiple neural network are established, thereby avoiding the assumption of conditional independence, so that the method is suitable for determining the software defect probability in a complex scenario, and the applicability of the software defect probability determination method is improved.

The present invention claims protection for a weight Bayesian classifier and an ontology mapping method for semantic ontology, which comprises the following steps: S1: Parsing the ontology: Parsing the ontology participating in the ontology mapping through the Jena package in Java, extracting concepts, and using the WordNet semantic dictionary to complete the expansion of concepts; S2: Determining weights using the harmony search algorithm: Introducing a dynamic weight mechanism to reduce the impact brought by its assumption of conditional independence of attributes; S3: Constructing a weight Bayesian classifier: Establishing a weight Bayesian classifier through the weights obtained in the previous step, converting the ontology mapping problem into a classification problem, calculating the maximum posterior probability to complete the classification; finally, saving the output classification result into the AllergoGraph database. The present invention adopts the harmony search algorithm, uses the classification error rate as the objective function, calculates the global optimal weight and assigns it to the weighted naive Bayesian classifier; the present invention proposes a new way of generating solutions to improve the optimization ability of the harmony search algorithm.