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).

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An important sampling method in Monte Carlo Markov chain

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.

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