Microbial biogeography mainly focuses on the distribution patterns of microorganisms and their driving mechanisms,and currently classical frequency mathematical and statistical methods are widely used in this field.In recent years,Bayesian inference has been continuously applied as an important random simulation mathematical statistics method for soil microbial biogeographical study.In this paper,we briefly introduce the differences between Bayesian inference and classical frequency mathematical statistics,and highlight the basic analytical process of Bayesian inference in soil microbial biogeographical study in terms of model construction,model fitting,and model improvement.We also evaluate the advantages,application potentials and development directions of Bayesian inference in this field and demonstrate the Bayesian inference process using simulated data with island biogeography theory as the research framework.Finally,we give an outlook on the application prospects of Bayesian inference in soil microbial biogeography.We believe that Bayesian inference will become one of the important tools for studying complex data and conducting model simulations in soil microbial biogeography.

soil microbial biogeography  /  Bayesian inference  /  classical frequency statistics  /  Markov chain Monte Carlo sampling