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Through incremental integration and independent research and development, build a method library of big data quality control, automatic modeling and analysis, data mining and interactive visualization, form a tool library with high reliability, high scalability, high efficiency and high fault tolerance, realize the integration and sharing of collaborative analysis methods of multi-source heterogeneous, multi-granularity, multi-phase, long-time series big data in three pole environment, as well as high Efficient and online big data analysis and processing.

  • Bayesian Method

    Edge probability (also known as prior probability) is the probability of an event. The edge probability is obtained by combining the unnecessary events in the final result into their total probability and eliminating them (sum the total probability for discrete random variables and integrate the total probability for continuous random variables). This is called marginalization. For example, the edge probability of a is expressed as P (a), and the edge probability of B is expressed as p (B).

    Installation: no installation required;

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    2019-10-16 471 View Details

  • MCMC

    The uncertainty in quantitatively estimating the model parameters plays a key role in improving the simulation accuracy of the model and identifying the structure of the model. The algorithm estimates the parameters of the eco-hydrological model based on the fusion of multi-source data of Bayesian method, which can effectively overcome the problem of "parameter equifinality" of the eco-hydrologic model. It provides a research framework for reducing the uncertainty of model parameters and identifying the error structural of the model.

    Installation: no installation required;

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    2019-08-02 785 View Details

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