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Bayesian Statistical Methods with PyMC3

This project is part of the liveProject series Time Series Forecasting with Bayesian Modeling.
prerequisites
intermediate Python (knowledge of pandas, NumPy, scikit-learn) • basics of time series methodologies
skills learned
manipulating a dataset with pandas • probabilistic time series analysis with PyMC3 • generating posterior distributions • modeling autoregressive processes
Michael Grogan
1 week · 4-6 hours per week · ADVANCED

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In this liveProject, you’ll use PyMC3 to generate a posterior distribution of hotel cancellations. This will allow you to build a predictive model that can update its predicted probabilities by incorporating new information. You’ll master methods for modelling mean and standard deviations based on the selected prior values, generating distributions to determine if data follows an AR(1) process, modeling of stochastic volatility and generalized linear modelling with PyMC3.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Michael Grogan
Michael Grogan is a data scientist with expertise in TensorFlow and time series analysis. His educational background is a Master's degree in Economics from University College Cork, Ireland. As such, much of his work has been in the domain of business intelligence, i.e. using machine learning technologies to develop solutions to a wide range of business problems. He has implemented time series solutions for organizations across a range of industries through the implementation of statistical analysis as well as more advanced machine learning methodologies. In addition, he has delivered numerous seminars and training courses in the areas of data science and machine learning, including for Manning and O'Reilly Media.

prerequisites

This liveProject is for data analysts with an intermediate understanding of time series analysis. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python knowledge (pandas, NumPy)
  • Intermediate scikit-learn
TECHNIQUES
  • Intermediate probability
  • Intermediate statistical theory
  • Intermediate time series methodologies

you will learn

In this liveProject, you’ll learn to generate Bayesian models based on numerous components of a dataset.

  • Generating posterior distributions
  • Modeling autoregressive processes
  • Volatility modeling
  • Bayesian generalized linear modeling

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
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While within the liveProject platform, get help from other participants and our expert mentors.
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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