This Jupyter Notebook implements a Bayesian model for (A) fitting the posterior distribution given the data and (B) predicting consumer spending behavior in an e-commerce company. The objective is to model the average amount of money customers spend per month, considering specific constraints without the actual data by a prior predictive model. We use Bayesian statistics and Python's PyMC library to carry out automated inference.
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This Jupyter notebook implements a Bayesian model for (A) fitting the posterior distribution given the data and (B) predicting consumer spending behavior in an e-commerce company. The objective is to model the average amount of money customers spend per month, considering specific constraints without the actual data by prior predictive model.
gkukish/Bayesian-Inference-with-PyMC
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This Jupyter notebook implements a Bayesian model for (A) fitting the posterior distribution given the data and (B) predicting consumer spending behavior in an e-commerce company. The objective is to model the average amount of money customers spend per month, considering specific constraints without the actual data by prior predictive model.
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