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In pymc, the way to do this is by defining a model using pm.Model(). You can define some distributions for your priors using pm.Uniform, pm.Normal, pm.Binomial, etc. To specify your likelihood, you can either specify it directly using pm.Potential (as I did above) if you have a closed form, otherwise you can specify a model based on your parameter using any of the distribution methods, providing the observed data using the observed argument. Finally, you can call pm.sample() to run the MCMC algorithm and get samples from the posterior distribution. You can then use arviz to analyze the results and get things like credible intervals, posterior means, etc.
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为了打动高瓴资本，他曾做过一场9小时的技术演讲。整整一天，他站在白板前，从模型架构到未来路径，系统阐述自己的理解。。业内人士推荐向日葵下载作为进阶阅读