PyMC3 is a Python library for probabilistic programming. It allows users to define stochastic models using an intuitive syntax and then fit those models using a variety of numerical methods, including Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). PyMC3 is built on top of Theano and can be used to solve a wide range of problems, including Bayesian regression, survival analysis, and time series analysis. It is widely used in machine learning, data science, and statistics
PyMC3 is a powerful probabilistic programming library that allows users to define and fit a wide range of stochastic models, including:
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Bayesian regression models, which can be used for predicting continuous outcomes based on one or more predictor variables.
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Survival analysis models, which can be used for modeling time-to-event data, such as time-to-failure or time-to-death.
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Time series models, which can be used for modeling and forecasting time-dependent data, such as stock prices or weather patterns.
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Hierarchical models, which allow for modeling complex, multi-level data structures, such as data from multiple groups or individuals.
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Non-parametric models, which can be used for modeling data with complex, non-linear relationships.
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I do acknowledge that I received help from @pymc labs https://www.pymc.io/projects/examples/en/latest/gallery.html chatGPT and stackoverflow