Фото: Рамиль Ситдиков / РИА Новости
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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与同业横向对比显示，华夏银行的这两项增速指标在已公布业绩快报的五家股份制银行中均处于末位：不仅是唯一一家营收与净利润同时负增长的银行，其下滑幅度也最为显著。，详情可参考WhatsApp API教程,WhatsApp集成指南,海外API使用
这种机制的兴起反映了储能行业正经历的重大变革。2026年起，越来越多的储能合同开始采用基于锂价指数的浮动定价模式。当碳酸锂价格波动超过预设幅度时，系统售价将自动调整，既保护了集成商利益，也获得了下游客户的认可。马成龙指出，这种协议实现了风险共担——作为电芯采购方，远东无法独自承担碳酸锂价格波动压力，联动机制让上下游共同分担风险。