Bayesian Maximum Entropy
  • Method description:
  • The method performs spatial distribution studies with the ability to fuse multiple aspects of data of different precision and quality, and divides these data into two aspects: (i) specialized data (KS): data that represent data related to the study area, including hard and soft data; (ii) universal knowledge/data (KG): data or knowledge used to describe the overall characteristics of the spatial random domain, such as general natural laws, empirical knowledge and hard data based on any order of statistical kinetic differences (e.g., mathematical expectation, covariance, variance, etc.). Based on these two aspects of data, the BME method is divided into two steps: (i) using KG, based on the maximum entropy principle, calculate the prior probability density function (pdf) of the distribution of unmeasured variables in the study area; (ii) using KS, based on Bayesian conditional probability, update the prior pdf obtained in the previous step to obtain the Based on the final posterior pdf, a variety of plots can be easily produced, such as prediction plots, probability distribution plots beyond a certain threshold, etc.

    • 安装方式: