Ecological inferences need structurally flexible statistical models to accommodate complex ecological phenomena. PyMC3 is a Probabilistic Programming Language (PPL) and allows for custom statistical distributions to build complex statistical models. This study used PyMC3 to implement Bayesian generalized Poisson (GP), zero-inflated GP, and hurdle GP regression models for over- and under-dispersed counts. The Bayesian GP regression models were fitted to simulated counts and real-world counts of over- and under-dispersion, respectively. Coefficient estimates of the Bayesian regression models were consistent with the known values used in the simulations and those of published work or models. Simulations demonstrated that Bayesian GP regression models with the NUTS sampler worked correctly for under-dispersed counts if the number of non-zero frequency classes was five or more. PyMC3 is not only flexible for building complex statistical models using custom likelihood functions, but also syntactically concise. The programming flexibility of PyMC3 can provide ecologists and environmental scientists with flexible, robust Bayesian computational platforms.

生态推论需要结构上灵活的统计模型来适应复杂的生态现象。PyMC3是一种概率编程语言（PPL），允许自定义统计分布来构建复杂的统计模型。这项研究使用PyMC3来实现贝叶斯广义泊松（GP），零膨胀GP和跨栏GP回归模型，以实现过度分散和分散不足的计数。贝叶斯GP回归模型分别适用于过度分散和分散不足的模拟计数和实际计数。贝叶斯回归模型的系数估计与模拟中使用的已知值以及已发表的工作或模型的已知值一致。仿真表明，如果非零频率类的数量为五个或更多，则使用NUTS采样器的贝叶斯GP回归模型可以正确处理分散不足的计数。PyMC3不仅可以灵活地使用自定义似然函数构建复杂的统计模型，而且在语法上也非常简洁。PyMC3的编程灵活性可以为生态学家和环境科学家提供灵活，强大的贝叶斯计算平台。